Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Neural network learning of optimal Kalman prediction and control
Linsker, Ralph
2008-01-01
Although there are many neural network (NN) algorithms for prediction and for control, and although methods for optimal estimation (including filtering and prediction) and for optimal control in linear systems were provided by Kalman in 1960 (with nonlinear extensions since then), there has been, to my knowledge, no NN algorithm that learns either Kalman prediction or Kalman control (apart from the special case of stationary control). Here we show how optimal Kalman prediction and control (KPC), as well as system identification, can be learned and executed by a recurrent neural network composed of linear-response nodes, using as input only a stream of noisy measurement data. The requirements of KPC appear to impose significant constraints on the allowed NN circuitry and signal flows. The NN architecture implied by these constraints bears certain resemblances to the local-circuit architecture of mammalian cerebral cortex. We discuss these resemblances, as well as caveats that limit our current ability to draw ...
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
Stable MIMO Constrained Predictive Control with Steady state Objective Optimization
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A two-stage multi-objective optimization model-predictive control algorithms(MPC) strategy is pre sented. A domain MPC controller with input constraints is used to increase freedom for steady-state objective and enhance stabilization of the controller. A steady-state objective optimization algorithm oriented to transient process is adopted to realize optimization of objectives else than dynamic control. It is proved that .the stabilization for both dynamic control and steady-state objective optimization can be guaranteed. The theoretical results are demonstrated and discussed using a distillation tower as the model. Theoretical analysis and simulation results show that this control strategy is efficient and provides a good strategic solution to practical process control.
A Computationally Efficient Aggregation Optimization Strategy of Model Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Predictive Control (MPC) is a popular technique and has been successfully used in various industrial applications. However, the big drawback of MPC involved in the formidable on-line computational effort limits its applicability to relatively slow and/or small processes with a moderate number of inputs. This paper develops an aggregation optimization strategy for MPC that can improve the computational efficiency of MPC. For the regulation problem, an input decaying aggregation optimization algorithm is presented by aggregating all the original optimized variables on control horizon with the decaying sequence in respect of the current control action.
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
Multi-objective optimization framework for networked predictive controller design.
Das, Sourav; Das, Saptarshi; Pan, Indranil
2013-01-01
Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives.
Adapting Predictive Feedback Chaos Control for Optimal Convergence Speed
Bick, Christian; Kolodziejski, Christoph
2012-01-01
Stabilizing unstable periodic orbits in a chaotic invariant set not only reveals information about its structure but also leads to various interesting applications. For the successful application of a chaos control scheme, convergence speed is of crucial importance. Here we present a predictive feedback chaos control method that adapts a control parameter online to yield optimal asymptotic convergence speed. We study the adaptive control map both analytically and numerically and prove that it converges at least linearly to a value determined by the spectral radius of the control map at the periodic orbit to be stabilized. The method is easy to implement algorithmically and may find applications for adaptive online control of biological and engineering systems.
Constrained Fuzzy Predictive Control Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Oussama Ait Sahed
2015-01-01
Full Text Available A fuzzy predictive controller using particle swarm optimization (PSO approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.
IMPORTANCE OF KINETIC MEASURES IN TRAJECTORY PREDICTION WITH OPTIMAL CONTROL
Directory of Open Access Journals (Sweden)
Ömer GÜNDOĞDU
2001-02-01
Full Text Available A two-dimensional sagittally symmetric human-body model was established to simulate an optimal trajectory for manual material handling tasks. Nonlinear control techniques and genetic algorithms were utilized in the optimizations to explore optimal lifting patterns. The simulation results were then compared with the experimental data. Since the kinetic measures such as joint reactions and moments are vital parameters in injury determination, the importance of comparing kinetic measures rather than kinematical ones was emphasized.
A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
We introduce the predictive control theory into the study of chaos control and propose a direct optimizing predictive control algorithm based on a neural network model. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems.The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.
Distributed Model Predictive Control of A Wind Farm for Optimal Active Power Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai;
2015-01-01
This paper presents a dynamic discrete-time Piece- Wise Affine (PWA) model of a wind turbine for the optimal active power control of a wind farm. The control objectives include both the power reference tracking from the system operator and the wind turbine mechanical load minimization. Instead......, which combines the clustering, linear identification and pattern recognition techniques. The developed model, consisting of 47 affine dynamics, is verified by the comparison with a widely-used nonlinear wind turbine model. It can be used as a predictive model for the Model Predictive Control (MPC......) or other advanced optimal control applications of a wind farm....
Energy Technology Data Exchange (ETDEWEB)
Medeiros, Jose Antonio Carlos Canedo; Machado, Marcelo Dornellas; Lima, Alan Miranda M. de; Schirru, Roberto [Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ-RJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoracao de Processos
2007-07-01
Predictive control systems are control systems that use a model of the controlled system (plant), used to predict the future behavior of the plant allowing the establishment of an anticipative control based on a future condition of the plant, and an optimizer that, considering a future time horizon of the plant output and a recent horizon of the control action, determines the controller's outputs to optimize a performance index of the controlled plant. The predictive control system does not require analytical models of the plant; the model of predictor of the plant can be learned from historical data of operation of the plant. The optimizer of the predictive controller establishes the strategy of the control: the minimization of a performance index (objective function) is done so that the present and future control actions are computed in such a way to minimize the objective function. The control strategy, implemented by the optimizer, induces the formation of an optimal control mechanism whose effect is to reduce the stabilization time, the 'overshoot' and 'undershoot', minimize the control actuation so that a compromise among those objectives is attained. The optimizer of the predictive controller is usually implemented using gradient-based algorithms. In this work we use the Particle Swarm Optimization algorithm (PSO) in the optimizer component of a predictive controller applied in the control of the xenon oscillation of a pressurized water reactor (PWR). The PSO is a stochastic optimization technique applied in several disciplines, simple and capable of providing a global optimal for high complexity problems and difficult to be optimized, providing in many cases better results than those obtained by other conventional and/or other artificial optimization techniques. (author)
A novel trajectory prediction control for proximate time-optimal digital control DC—DC converters
Qing, Wang; Ning, Chen; Shen, Xu; Weifeng, Sun; Longxing, Shi
2014-09-01
The purpose of this paper is to present a novel trajectory prediction method for proximate time-optimal digital control DC—DC converters. The control method provides pre-estimations of the duty ratio in the next several switching cycles, so as to compensate the computational time delay of the control loop and increase the control loop bandwidth, thereby improving the response speed. The experiment results show that the fastest transient response time of the digital DC—DC with the proposed prediction is about 8 μs when the load current changes from 0.6 to 0.1 A.
Energy Technology Data Exchange (ETDEWEB)
Kim, Jae Hwan; Kim, Ju Hyun; Park, Sun Ho; Yoo, Kwae Hwan; Kim, Dae Seop; Na, Man Gyun [Chosun Univ., Gwanggu (Korea, Republic of)
2012-10-15
Until now, nuclear power has been only used for the base load power operation. However, current nuclear power plants are recognized as the most reasonable energy source. As a result, the proportion of nuclear power has being grown increasingly. Therefore, load following operation of a nuclear power plant should be an essential option. Most of the existing nuclear power plants perform reactor operation by varying the boron concentration in the coolant. But it is hard to respond quickly to demands for the power changes. In case of using the control rods, reactivity control is easy, but axial power distribution control is very hard because it has very complex and nonlinear dynamic characteristics. In this study, we have introduced a Model Predictive Control (MPC) method to control the average coolant temperature and Axial Shape Index (ASI) automatically at the same time, and we have improved the performance of controller by applying the Genetic Algorithm (GA) to optimize the control rod movement.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.
2010-09-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Particle Swarm Optimization based predictive control of Proton Exchange Membrane Fuel Cell (PEMFC)
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Proton Exchange Membrane Fuel Cells (PEMFCs) are the main focus of their current development as power sources because they are capable of higher power density and faster start-up than other fuel cells. The humidification system and output performance of PEMFC stack are briefly analyzed. Predictive control of PEMFC based on Support Vector Regression Machine (SVRM) is presented and the SVRM is constructed. The processing plant is modelled on SVRM and the predictive control law is obtained by using Particle Swarm Optimization (PSO). The simulation and the results showed that the SVRM and the PSO receding optimization applied to the PEMFC predictive control yielded good performance.
Robust, Optimal, Predictive, and Integrated Road Traffic Control: Research proposal
Van de Weg, G.S.; Hegyi, A.; Hoogendoorn, S.P.
2014-01-01
The development of control strategies for traffic lights, ramp metering installations, and variable speed limits to improve the throughput of road traffic networks can contribute to a more efficient use of road networks. In this project, a hierarchical controller will be developed for the improvemen
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
A predictive control framework for optimal energy extraction of wind farms
Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.
2016-09-01
This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.
Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler
Directory of Open Access Journals (Sweden)
Zhenhao Tang
2017-01-01
Full Text Available To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.
Optimal control predicts human performance on objects with internal degrees of freedom.
Directory of Open Access Journals (Sweden)
Arne J Nagengast
2009-06-01
Full Text Available On a daily basis, humans interact with a vast range of objects and tools. A class of tasks, which can pose a serious challenge to our motor skills, are those that involve manipulating objects with internal degrees of freedom, such as when folding laundry or using a lasso. Here, we use the framework of optimal feedback control to make predictions of how humans should interact with such objects. We confirm the predictions experimentally in a two-dimensional object manipulation task, in which subjects learned to control six different objects with complex dynamics. We show that the non-intuitive behavior observed when controlling objects with internal degrees of freedom can be accounted for by a simple cost function representing a trade-off between effort and accuracy. In addition to using a simple linear, point-mass optimal control model, we also used an optimal control model, which considers the non-linear dynamics of the human arm. We find that the more realistic optimal control model captures aspects of the data that cannot be accounted for by the linear model or other previous theories of motor control. The results suggest that our everyday interactions with objects can be understood by optimality principles and advocate the use of more realistic optimal control models for the study of human motor neuroscience.
Optimal Parameter Tuning in a Predictive Nonlinear Control Method for a Mobile Robot
Directory of Open Access Journals (Sweden)
D. Hazry
2006-01-01
Full Text Available This study contributes to a new optimal parameter tuning in a predictive nonlinear control method for stable trajectory straight line tracking with a non-holonomic mobile robot. In this method, the focus lies in finding the optimal parameter estimation and to predict the path that the mobile robot will follow for stable trajectory straight line tracking system. The stability control contains three parameters: 1 deflection parameter for the traveling direction of the mobile robot 2 deflection parameter for the distance across traveling direction of the mobile robot and 3 deflection parameter for the steering angle of the mobile robot . Two hundred and seventy three experimental were performed and the results have been analyzed and described herewith. It is found that by using a new optimal parameter tuning in a predictive nonlinear control method derived from the extension of kinematics model, the movement of the mobile robot is stabilized and adhered to the reference posture
DEFF Research Database (Denmark)
Weerts, Hermanus H. M.; Shafiei, Seyed Ehsan; Stoustrup, Jakob
2014-01-01
A new formulation of model predictive control for supermarket refrigeration systems is proposed to facilitate the regulatory power services as well as energy cost optimization of such systems in the smart grid. Nonlinear dynamics existed in large-scale refrigeration plants challenges the predictive...... control design. It is however shown that taking into account the knowledge of different time scales in the dynamical subsystems makes possible a linear formulation of a centralized predictive controller. A realistic scenario of regulatory power services in the smart grid is considered and formulated...
PLIO: a generic tool for real-time operational predictive optimal control of water networks.
Cembrano, G; Quevedo, J; Puig, V; Pérez, R; Figueras, J; Verdejo, J M; Escaler, I; Ramón, G; Barnet, G; Rodríguez, P; Casas, M
2011-01-01
This paper presents a generic tool, named PLIO, that allows to implement the real-time operational control of water networks. Control strategies are generated using predictive optimal control techniques. This tool allows the flow management in a large water supply and distribution system including reservoirs, open-flow channels for water transport, water treatment plants, pressurized water pipe networks, tanks, flow/pressure control elements and a telemetry/telecontrol system. Predictive optimal control is used to generate flow control strategies from the sources to the consumer areas to meet future demands with appropriate pressure levels, optimizing operational goals such as network safety volumes and flow control stability. PLIO allows to build the network model graphically and then to automatically generate the model equations used by the predictive optimal controller. Additionally, PLIO can work off-line (in simulation) and on-line (in real-time mode). The case study of Santiago-Chile is presented to exemplify the control results obtained using PLIO off-line (in simulation).
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.
2015-01-01
In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP...
Directory of Open Access Journals (Sweden)
L. I. Rozonoer
1999-01-01
Full Text Available Necessary and sufficient conditions for existence of optimal control for all initial data are proved for LQ-optimization problem. If these conditions are fulfilled, necessary and sufficient conditions of optimality are formulated. Basing on the results, some general hypotheses on optimal control in terms of Pontryagin's maximum condition and Bellman's equation are proposed.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik;
2015-01-01
In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...
Real-time economic optimization for a fermentation process using Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp
2014-01-01
Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process...... and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E......-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem...
Optimal control in a micro gas grid of prosumers using Model Predictive Control
Alkano, Desti; Nefkens, W.J.; Scherpen, Jacqueline M.A.; Volkerts, M.
This paper studies the optimal control of a micro grid of biogas prosumers equipped with local storage devices. Excess biogas can be upgraded and injected into the low- pressure gas grid or, alternatively, shipped per lorry to be used elsewhere in an effort to create revenue. The aim of the control
Optimal control in a micro gas grid of prosumers using Model Predictive Control
Alkano, Desti; Nefkens, W.J.; Scherpen, Jacqueline M.A.; Volkerts, M.
2014-01-01
This paper studies the optimal control of a micro grid of biogas prosumers equipped with local storage devices. Excess biogas can be upgraded and injected into the low- pressure gas grid or, alternatively, shipped per lorry to be used elsewhere in an effort to create revenue. The aim of the control
Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.
Deshpande, Sunil; Nandola, Naresh N; Rivera, Daniel E; Younger, Jarred W
2014-12-01
The term adaptive intervention is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.
Tofighi, Elham; Mahdizadeh, Amin
2016-09-01
This paper addresses the problem of automatic tuning of weighting coefficients for the nonlinear model predictive control (NMPC) of wind turbines. The choice of weighting coefficients in NMPC is critical due to their explicit impact on efficiency of the wind turbine control. Classically, these weights are selected based on intuitive understanding of the system dynamics and control objectives. The empirical methods, however, may not yield optimal solutions especially when the number of parameters to be tuned and the nonlinearity of the system increase. In this paper, the problem of determining weighting coefficients for the cost function of the NMPC controller is formulated as a two-level optimization process in which the upper- level PSO-based optimization computes the weighting coefficients for the lower-level NMPC controller which generates control signals for the wind turbine. The proposed method is implemented to tune the weighting coefficients of a NMPC controller which drives the NREL 5-MW wind turbine. The results are compared with similar simulations for a manually tuned NMPC controller. Comparison verify the improved performance of the controller for weights computed with the PSO-based technique.
Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2014-01-01
In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce......-shooting method combined with a quasi-Newton Sequential Quadratic Programming (SQP) algorithm and the adjoint method for computation of gradients. The E-NMPC improves the cost of spray drying by 26.7% compared to conventional PI control in our simulations....
Optimization and Optimal Control
Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider
2010-01-01
During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou
Fuzzy Constrained Predictive Optimal Control of High Speed Train with Actuator Dynamics
Directory of Open Access Journals (Sweden)
Xi Wang
2016-01-01
Full Text Available We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs. Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.
Model-predictive control and real-time optimization of a cat cracker unit
Directory of Open Access Journals (Sweden)
Stig Strand
1997-04-01
Full Text Available A project for control and optimization of the Residual Catalytic Cracking Process at the Mongstad refinery is near completion. Four model-predictive control applications have been successfully implemented, using the IDCOM control software from Setpoint Inc. The most attractive feature of the controller is the well-defined control prioritizing hierarchy, and the linear impulse-response models have proved to give satisfactory performance on this process. Excitation and identification of the dynamic models proved to be a difficult task, and careful design and monitoring of the tests was mandatory in order to produce good results. Multi-variable Pseudo Random Binary Test Sequences were used for the excitation. Technical performance and operator acceptance of the new control functions have been good, but it is realized that a continuing effort is needed to fine-tune and maintain such functions.
Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory
Energy Technology Data Exchange (ETDEWEB)
Gregor P. Henze; Moncef Krarti
2005-09-30
Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very
Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P
2016-01-01
This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...
An optimal current observer for predictive current controlled buck DC-DC converters.
Min, Run; Chen, Chen; Zhang, Xiaodong; Zou, Xuecheng; Tong, Qiaoling; Zhang, Qiao
2014-05-19
In digital current mode controlled DC-DC converters, conventional current sensors might not provide isolation at a minimized price, power loss and size. Therefore, a current observer which can be realized based on the digital circuit itself, is a possible substitute. However, the observed current may diverge due to the parasitic resistors and the forward conduction voltage of the diode. Moreover, the divergence of the observed current will cause steady state errors in the output voltage. In this paper, an optimal current observer is proposed. It achieves the highest observation accuracy by compensating for all the known parasitic parameters. By employing the optimal current observer-based predictive current controller, a buck converter is implemented. The converter has a convergently and accurately observed inductor current, and shows preferable transient response than the conventional voltage mode controlled converter. Besides, costs, power loss and size are minimized since the strategy requires no additional hardware for current sensing. The effectiveness of the proposed optimal current observer is demonstrated experimentally.
Woldekidan, Korbaga
This dissertation aims at developing a novel and systematic approach to apply Model Predictive Control (MPC) to improve energy efficiency and indoor environmental quality in office buildings. Model predictive control is one of the advanced optimal control approaches that use models to predict the behavior of the process beyond the current time to optimize the system operation at the present time. In building system, MPC helps to exploit buildings' thermal storage capacity and to use the information on future disturbances like weather and internal heat gains to estimate optimal control inputs ahead of time. In this research the major challenges of applying MPC to building systems are addressed. A systematic framework has been developed for ease of implementation. New methods are proposed to develop simple and yet reasonably accurate models that can minimize the MPC development effort as well as computational time. The developed MPC is used to control a detailed building model represented by whole building performance simulation tool, EnergyPlus. A co-simulation strategy is used to communicate the MPC control developed in Matlab platform with the case building model in EnergyPlus. The co-simulation tool used (MLE+) also has the ability to talk to actual building management systems that support the BACnet communication protocol which makes it easy to implement the developed MPC control in actual buildings. A building that features an integrated lighting and window control and HVAC system with a dedicated outdoor air system and ceiling radiant panels was used as a case building. Though this study is specifically focused on the case building, the framework developed can be applied to any building type. The performance of the developed MPC was compared against a baseline control strategy using Proportional Integral and Derivative (PID) control. Various conventional and advanced thermal comfort as well as ventilation strategies were considered for the comparison. These
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai
2016-01-01
This paper presents the Distributed Model Predictive Control (D-MPC) of a wind farm equipped with fast and short-term Energy Storage System (ESS) for optimal active power control using the fast gradient method via dual decomposition. The primary objective of the D-MPC control of the wind farm...... is power reference tracking from system operators. Besides, by optimal distribution of the power references to individual wind turbines and the ESS unit, the wind turbine mechanical loads are alleviated. With the fast gradient method, the convergence rate of the DMPC is significantly improved which leads...... is independent from the wind farm size and is suitable for the real-time control of the wind farm with ESS....
Energy Technology Data Exchange (ETDEWEB)
Hindi, H.; Prabhakar, S.; Fox, J.; Teytelman, D.
1997-12-01
The authors present a technique for the design and verification of efficient bunch-by-bunch controllers for damping longitudinal multibunch instabilities. The controllers attempt to optimize the use of available feedback amplifier power--one of the most expensive components of a feedback system--and define the limits of closed loop system performance. The design technique alternates between analytic computation of single bunch optimal controllers and verification on a multibunch numerical simulator. The simulator identifies unstable coupled bunch modes and predicts their growth and damping rates. The results from the simulator are shown to be in reasonable agreement with analytical calculations based on the single bunch model. The technique is then used to evaluate the performance of a variety of controllers proposed for PEP-II.
Yenkie, Kirti M; Diwekar, Urmila
2014-08-21
in vitro fertilization (IVF) is one of the most highly pursued assisted reproductive technologies (ART) worldwide. IVF procedure is divided into four stages: Superovulation, Egg-retrieval, Insemination/Fertilization and Embryo transfer. Among these superovulation is the most crucial stage since it involves external injection of hormones to stimulate development and maturation of multiple follicles or oocytes. Although numerous advancements have been made in IVF procedures, little attention has been given to modifying the existing protocols based on a 'patient specific' predictive model. A model for follicle growth and number change as a function of the injected hormones and patient characteristics has been developed and validated for data available on 50 superovulation cycles. The model has 9 patient specific parameters which can be determined from the initial 2 days of observation and can help in projecting the superovulation outcome for the ongoing cycle. Based on this model, the dosage of the hormones to stimulate multiple ovulation or follicle growth is predicted by using the theory of optimal control. The objective of successful superovulation is to obtain maximum number of mature oocytes/follicles within a particular size range. Using the mathematical model of follicle growth dynamics and optimal control theory, optimal dose and frequency of medication customized for each patient (n=5) is predicted for obtaining the desired result. The results indicate a better final day follicle size distribution when the dosage of the hormones is varied by some amounts as compared to the actual dosage given to the patient in the existing cycles. This ensures a better success rate for the superovulation cycles and reduces the costs of excess medication and daily monitoring. The idea is to provide the medical practitioners with a guideline for planned treatment, for a procedure currently based on trial and error in order to get better success rates. Copyright © 2014 Elsevier
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint
Energy Technology Data Exchange (ETDEWEB)
Raszmann, Emma; Baker, Kyri; Shi, Ying; Christensen, Dane
2017-02-22
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control
Energy Technology Data Exchange (ETDEWEB)
Baker, Kyri A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Shi, Ying [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Christensen, Dane T [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Raszmann, Emma [University of Pittsburgh
2017-06-01
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
Predictive Optimal Control of Active and Passive Building Thermal Storage Inventory
Energy Technology Data Exchange (ETDEWEB)
Gregor P. Henze; Moncef Krarti
2005-09-30
Cooling of commercial buildings contributes significantly to the peak demand placed on an electrical utility grid. Time-of-use electricity rates encourage shifting of electrical loads to off-peak periods at night and weekends. Buildings can respond to these pricing signals by shifting cooling-related thermal loads either by precooling the building's massive structure or the use of active thermal energy storage systems such as ice storage. While these two thermal batteries have been engaged separately in the past, this project investigated the merits of harnessing both storage media concurrently in the context of predictive optimal control. To pursue the analysis, modeling, and simulation research of Phase 1, two separate simulation environments were developed. Based on the new dynamic building simulation program EnergyPlus, a utility rate module, two thermal energy storage models were added. Also, a sequential optimization approach to the cost minimization problem using direct search, gradient-based, and dynamic programming methods was incorporated. The objective function was the total utility bill including the cost of reheat and a time-of-use electricity rate either with or without demand charges. An alternative simulation environment based on TRNSYS and Matlab was developed to allow for comparison and cross-validation with EnergyPlus. The initial evaluation of the theoretical potential of the combined optimal control assumed perfect weather prediction and match between the building model and the actual building counterpart. The analysis showed that the combined utilization leads to cost savings that is significantly greater than either storage but less than the sum of the individual savings. The findings reveal that the cooling-related on-peak electrical demand of commercial buildings can be considerably reduced. A subsequent analysis of the impact of forecasting uncertainty in the required short-term weather forecasts determined that it takes only very
Directory of Open Access Journals (Sweden)
Suksan Tiyarachakun
2014-01-01
Full Text Available This paper presents a novel harmonic identification algorithm of shunt active power filter for balanced and unbalanced three-phase systems based on the instantaneous power theory called instantaneous power theory with Fourier. Moreover, the optimal design of predictive current controller using an artificial intelligence technique called adaptive Tabu search is also proposed in the paper. These enhancements of the identification and current control parts are the aim of the good performance for shunt active power filter. The good results for harmonic mitigation using the proposed ideas in the paper are confirmed by the intensive simulation using SPS in SIMULINK. The simulation results show that the enhanced shunt active power filter can provide the minimum %THD (Total Harmonic Distortion of source currents and unity power factor after compensation. In addition, the %THD also follows the IEEE Std.519-1992.
Directory of Open Access Journals (Sweden)
Guo Jiuwang
2015-01-01
Full Text Available Because of the randomness and fluctuation of wind energy, as well as the impact of strongly nonlinear characteristic of variable speed constant frequency (VSCF wind power generation system with doubly fed induction generators (DFIG, traditional active power control strategies are difficult to achieve high precision control and the output power of wind turbines is more fluctuated. In order to improve the quality of output electric energy of doubly fed wind turbines, on the basis of analyzing the operating principles and dynamic characteristics of doubly fed wind turbines, this paper proposes a new active power optimal control method of doubly fed wind turbines based on predictive control theory. This method uses state space model of wind turbines, based on the prediction of the future state of wind turbines, moves horizon optimization, and meanwhile, gets the control signals of pitch angle and generator torque. Simulation results show that the proposed control strategies can guarantee the utilization efficiency for wind energy. Simultaneously, they can improve operation stability of wind turbines and the quality of electric energy.
Predicting the Motions and Forces of Wearable Robotic Systems Using Optimal Control
Directory of Open Access Journals (Sweden)
Matthew Millard
2017-08-01
Full Text Available Wearable robotic systems are being developed to prevent injury to the low back. Designing a wearable robotic system is challenging because it is difficult to predict how the exoskeleton will affect the movement of the wearer. To aid the design of exoskeletons, we formulate and numerically solve an optimal control problem (OCP to predict the movements and forces of a person as they lift a 15 kg box from the ground both without (human-only OCP and with (with-exo OCP the aid of an exoskeleton. We model the human body as a sagittal-plane multibody system that is actuated by agonist and antagonist pairs of muscle torque generators (MTGs at each joint. Using the literature as a guide, we have derived a set of MTGs that capture the active torque–angle, passive torque–angle, and torque–velocity characteristics of the flexor and extensor groups surrounding the hip, knee, ankle, lumbar spine, shoulder, elbow, and wrist. Uniquely, these MTGs are continuous to the second derivative and so are compatible with gradient-based optimization. The exoskeleton is modeled as a rigid-body mechanism that is actuated by a motor at the hip and the lumbar spine and is coupled to the wearer through kinematic constraints. We evaluate our results by comparing our predictions with experimental recordings of a human subject. Our results indicate that the predicted peak lumbar-flexion angles and extension torques of the human-only OCP are within the range reported in the literature. The results of the with-exo OCP indicate that the exoskeleton motors should provide relatively little support during the descent to the box but apply a substantial amount of support during the ascent phase. The support provided by the lumbar motor is similar in shape to the net moment generated at the L5/S1 joint by the body; however, the support of the hip motor is more complex because it is coupled to the passive forces that are being generated by the hip extensors of the human subject
Institute of Scientific and Technical Information of China (English)
JIANG Tie-zheng; CHEN Chen; CAO Guo-yun
2006-01-01
The main objectives of this paper are to simultaneously improve power system damping and to maintain voltage at the static var compensator (SVC) location bus simultaneously.A new controller for SVC with closed-form analytic solution nonlinear optimal predictive control (NOPC) law was presented.The controller does not require online optimization and the huge calculation burden can be avoided,so that the demand of real-time control can be satisfied.In addition,there are only two design parameters,which are the predictive period and control order;so it is easy to implement and test in practical use.Simulation results have shown that the controller can not only attenuate power system oscillation effectively but can also maintain voltage at the SVC bus location.
MyoLin, Nay; Rutten, Martine; van de Giesen, Nick
2016-04-01
Flooding is a common natural disaster in the world. Construction of reservoirs, sluice gates, dikes, embankments and sea walls are implemented to minimize loss of life and property in a flood event. Rather than completely relying on large structural measures, non-structural measures such as real time control of a reservoir system can also improve flood prevention and water supply in a river basin. In this paper, we present the optimal operation of a multi-reservoir system by using Model Predictive Control (MPC) and particular attention is focused on flood mitigation of the Sittaung River Basin, Myanmar. The main challenges are non-linearity in the dynamic behavior of the water system and exponential growth of computational complexity with the state and control dimension. To deal with an issue related to non-linearity, we applied simplified internal model based on linearization scheme with a large grid length. For solving curse of dimensionality, we utilize the reduced model in which the states of the system are reduced by considering outflows from uncontrolled catchments as disturbances in the water system. We also address the computational time for real time control by using large time step scheme. Simulation results indicate that this model is able to use for real time control of a reservoir system addressing trade-offs between the multiple objectives.
Ekkachai, Kittipong; Nilkhamhang, Itthisek
2016-11-01
In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.
Gaborit, Étienne; Anctil, François; Vanrolleghem, Peter A.; Pelletier, Geneviève
2013-04-01
Dry detention ponds have been widely implemented in U.S.A (National Research Council, 1993) and Canada (Shammaa et al. 2002) to mitigate the impacts of urban runoff on receiving water bodies. The aim of such structures is to allow a temporary retention of the water during rainfall events, decreasing runoff velocities and volumes (by infiltration in the pond) as well as providing some water quality improvement from sedimentation. The management of dry detention ponds currently relies on static control through a fixed pre-designed limitation of their maximum outflow (Middleton and Barrett 2008), for example via a proper choice of their outlet pipe diameter. Because these ponds are designed for large storms, typically 1- or 2-hour duration rainfall events with return periods comprised between 5 and 100 years, one of their main drawbacks is that they generally offer almost no retention for smaller rainfall events (Middleton and Barrett 2008), which are by definition much more common. Real-Time Control (RTC) has a high potential for optimizing retention time (Marsalek 2005) because it allows adopting operating strategies that are flexible and hence more suitable to the prevailing fluctuating conditions than static control. For dry ponds, this would basically imply adapting the outlet opening percentage to maximize water retention time, while being able to open it completely for severe storms. This study developed several enhanced RTC scenarios of a dry detention pond located at the outlet of a small urban catchment near Québec City, Canada, following the previous work of Muschalla et al. (2009). The catchment's runoff quantity and TSS concentration were simulated by a SWMM5 model with an improved wash-off formulation. The control procedures rely on rainfall detection and measures of the pond's water height for the reactive schemes, and on rainfall forecasts in addition to these variables for the predictive schemes. The automatic reactive control schemes implemented
Integrated controls design optimization
Lou, Xinsheng; Neuschaefer, Carl H.
2015-09-01
A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.
DEFF Research Database (Denmark)
Koch-Ciobotaru, Cosmin; Isleifsson, Fridrik Rafn; Gehrke, Oliver
2012-01-01
the effects of their large penetration in the distribution grid and reduces overloading the grid capacity, which is an increasing problem for the power system. The controller uses 24 hour prediction data for the ambient temperature, the solar irradiance, and for the PV output power. Simulation results...... of a thermostatic controller, a MPC with grid price optimization, and the proposed MPC are presented and discussed....
Galelli, Stefano; Goedbloed, Albert; Schmitter, Petra; Castelletti, Andrea
2014-05-01
Urban water reservoirs are a viable adaptation option to account for increasing drinking water demand of urbanized areas as they allow storage and re-use of water that is normally lost. In addition, the direct availability of freshwater reduces pumping costs and diversifies the portfolios of drinking water supply. Yet, these benefits have an associated twofold cost. Firstly, the presence of large, impervious areas increases the hydraulic efficiency of urban catchments, with short time of concentration, increased runoff rates, losses of infiltration and baseflow, and higher risk of flash floods. Secondly, the high concentration of nutrients and sediments characterizing urban discharges is likely to cause water quality problems. In this study we propose a new control scheme combining Model Predictive Control (MPC), hydro-meteorological forecasts and dynamic model emulation to design real-time operating policies that conjunctively optimize water quantity and quality targets. The main advantage of this scheme stands in its capability of exploiting real-time hydro-meteorological forecasts, which are crucial in such fast-varying systems. In addition, the reduced computational requests of the MPC scheme allows coupling it with dynamic emulators of water quality processes. The approach is demonstrated on Marina Reservoir, a multi-purpose reservoir located in the heart of Singapore and characterized by a large, highly urbanized catchment with a short (i.e. approximately one hour) time of concentration. Results show that the MPC scheme, coupled with a water quality emulator, provides a good compromise between different operating objectives, namely flood risk reduction, drinking water supply and salinity control. Finally, the scheme is used to assess the effect of source control measures (e.g. green roofs) aimed at restoring the natural hydrological regime of Marina Reservoir catchment.
Directory of Open Access Journals (Sweden)
Yaman Arkun
Full Text Available An optimization model is introduced in which proteins try to evade high energy regions of the folding landscape, and prefer low entropy loss routes during folding. We make use of the framework of optimal control whose convenient solution provides practical and useful insight into the sequence of events during folding. We assume that the native state is available. As the protein folds, it makes different set of contacts at different folding steps. The dynamic contact map is constructed from these contacts. The topology of the dynamic contact map changes during the course of folding and this information is utilized in the dynamic optimization model. The solution is obtained using the optimal control theory. We show that the optimal solution can be cast into the form of a Gaussian Network that governs the optimal folding dynamics. Simulation results on three examples (CI2, Sso7d and Villin show that folding starts by the formation of local clusters. Non-local clusters generally require the formation of several local clusters. Non-local clusters form cooperatively and not sequentially. We also observe that the optimal controller prefers "zipping" or small loop closure steps during folding. The folding routes predicted by the proposed method bear strong resemblance to the results in the literature.
Institute of Scientific and Technical Information of China (English)
Yong-gang PENG; Jun WANG; Wei WEI
2014-01-01
In view of the high energy consumption and low response speed of the traditional hydraulic system for an injection molding machine, a servo motor driven constant pump hydraulic system is designed for a precision injection molding process, which uses a servo motor, a constant pump, and a pressure sensor, instead of a common motor, a constant pump, a pressure pro-portion valve, and a flow proportion valve. A model predictive control strategy based on neurodynamic optimization is proposed to control this new hydraulic system in the injection molding process. Simulation results showed that this control method has good control precision and quick response.
Nominal model predictive control
Grüne, Lars
2013-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Nominal Model Predictive Control
Grüne, Lars
2014-01-01
5 p., to appear in Encyclopedia of Systems and Control, Tariq Samad, John Baillieul (eds.); International audience; Model Predictive Control is a controller design method which synthesizes a sampled data feedback controller from the iterative solution of open loop optimal control problems.We describe the basic functionality of MPC controllers, their properties regarding feasibility, stability and performance and the assumptions needed in order to rigorously ensure these properties in a nomina...
Genetically optimizing weather predictions
Potter, S. B.; Staats, Kai; Romero-Colmenero, Encarni
2016-07-01
humidity, air pressure, wind speed and wind direction) into a database. Built upon this database, we have developed a remarkably simple approach to derive a functional weather predictor. The aim is provide up to the minute local weather predictions in order to e.g. prepare dome environment conditions ready for night time operations or plan, prioritize and update weather dependent observing queues. In order to predict the weather for the next 24 hours, we take the current live weather readings and search the entire archive for similar conditions. Predictions are made against an averaged, subsequent 24 hours of the closest matches for the current readings. We use an Evolutionary Algorithm to optimize our formula through weighted parameters. The accuracy of the predictor is routinely tested and tuned against the full, updated archive to account for seasonal trends and total, climate shifts. The live (updated every 5 minutes) SALT weather predictor can be viewed here: http://www.saao.ac.za/ sbp/suthweather_predict.html
Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds
SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui
2017-05-01
The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive
Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds
SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui
2017-03-01
The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive
Zhang, Jianming
2016-11-25
An improved proportional-integral-derivative (PID) controller based on predictive functional control (PFC) is proposed and tested on the chamber pressure in an industrial coke furnace. The proposed design is motivated by the fact that PID controllers for industrial processes with time delay may not achieve the desired control performance because of the unavoidable model/plant mismatches, while model predictive control (MPC) is suitable for such situations. In this paper, PID control and PFC algorithm are combined to form a new PID controller that has the basic characteristic of PFC algorithm and at the same time, the simple structure of traditional PID controller. The proposed controller was tested in terms of set-point tracking and disturbance rejection, where the obtained results showed that the proposed controller had the better ensemble performance compared with traditional PID controllers.
Zou, Hongbo; Li, Haisheng
2017-03-01
Proportional-integral-derivative (PID) control is widely used in industry because of its simple structure and convenient implementation. However, PID control is suitable for small time delay systems; while if too large delay is encountered, PID control may not obtain the desired performance. Proportional-integral-proportional-derivative (PI-PD) control is a modified of PID control and can get improved control performance; however, due to the complex controller parameter tuning, the PI-PD control is used in a limited scope. Inspired by the advantage of predictive functional control (PFC), a new PI-PD control design using PFC optimization is proposed in this paper. The proposed method not only inherits the advantage of PFC, which does well in coping with the time delay, but also has the same structure as the PI-PD controller. The proposed method is tested on the preheated temperature control of crude oil in a fluidized catalytic cracking unit. The results show that the proposed controller improves control performance compared with typical PID control and PI-PD control.
Energy Technology Data Exchange (ETDEWEB)
Hindi, Haitham; Prabhakar, Shyam; Fox, John D.; Linscott, Ivan; Teytelman, Dmitri; /SLAC
2011-08-31
We present a technique for the design and verification of efficient bunch-by-bunch controllers for damping longitudinal multibunch instabilities. The controllers attempt to optimize the use of available feedback amplifier power - one of the most expensive components of a feedback system - and define the limits of the closed loop system performance. Our design technique alternates between analytic computation of single bunch optimal controllers and verification on a multibunch numerical simulator. The simulator uses PEP-II parameters and identifies unstable coupled bunch modes, their growth rates and their damping rates with feedback. The results from the simulator are shown to be in reasonable agreement with analytical calculations based on the single bunch model. The technique is then used to evaluate the performance of a variety of controllers proposed for PEP-II.
Porsa, Sina; Lin, Yi-Chung; Pandy, Marcus G
2016-08-01
The aim of this study was to compare the computational performances of two direct methods for solving large-scale, nonlinear, optimal control problems in human movement. Direct shooting and direct collocation were implemented on an 8-segment, 48-muscle model of the body (24 muscles on each side) to compute the optimal control solution for maximum-height jumping. Both algorithms were executed on a freely-available musculoskeletal modeling platform called OpenSim. Direct collocation converged to essentially the same optimal solution up to 249 times faster than direct shooting when the same initial guess was assumed (3.4 h of CPU time for direct collocation vs. 35.3 days for direct shooting). The model predictions were in good agreement with the time histories of joint angles, ground reaction forces and muscle activation patterns measured for subjects jumping to their maximum achievable heights. Both methods converged to essentially the same solution when started from the same initial guess, but computation time was sensitive to the initial guess assumed. Direct collocation demonstrates exceptional computational performance and is well suited to performing predictive simulations of movement using large-scale musculoskeletal models.
Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.
2014-10-01
In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.
Torres, Juan; Menéndez, José Manuel
2015-02-01
This paper establishes a real-time auto-exposure method to guarantee that surveillance cameras in uncontrolled light conditions take advantage of their whole dynamic range while provide neither under nor overexposed images. State-of-the-art auto-exposure methods base their control on the brightness of the image measured in a limited region where the foreground objects are mostly located. Unlike these methods, the proposed algorithm establishes a set of indicators based on the image histogram that defines its shape and position. Furthermore, the location of the objects to be inspected is likely unknown in surveillance applications. Thus, the whole image is monitored in this approach. To control the camera settings, we defined a parameters function (Ef ) that linearly depends on the shutter speed and the electronic gain; and is inversely proportional to the square of the lens aperture diameter. When the current acquired image is not overexposed, our algorithm computes the value of Ef that would move the histogram to the maximum value that does not overexpose the capture. When the current acquired image is overexposed, it computes the value of Ef that would move the histogram to a value that does not underexpose the capture and remains close to the overexposed region. If the image is under and overexposed, the whole dynamic range of the camera is therefore used, and a default value of the Ef that does not overexpose the capture is selected. This decision follows the idea that to get underexposed images is better than to get overexposed ones, because the noise produced in the lower regions of the histogram can be removed in a post-processing step while the saturated pixels of the higher regions cannot be recovered. The proposed algorithm was tested in a video surveillance camera placed at an outdoor parking lot surrounded by buildings and trees which produce moving shadows in the ground. During the daytime of seven days, the algorithm was running alternatively together
Directory of Open Access Journals (Sweden)
Mohammad Najafzadeh
2015-03-01
Full Text Available In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO. Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS. The testing results of performances were compared with the gene-expression programming (GEP, evolutionary polynomial regression (EPR model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.
Directory of Open Access Journals (Sweden)
Kamal Fu'ad
2013-09-01
Full Text Available Pada penelitian ini telah dibangun mode kontrol Model Predictive Control (MPC dengan metode optimasi Particle Swarm Optimization untuk mencari nilai terbaik pada parameter beban sinyal kontrol Wu dan sinyal control error W∆u yang kemudian diimplementasikan secara online pada rancang bangun system Quadruple Tank. Metode IMOPSO untuk MPC dengan nilai sinyal control Wu =0.0076 dan sinyal control error Wdu = 0.1221 menghasilkan respon system terbaik dengan maximum overshoot = 4% error steady state 1% settling time 55 detik dibandingkan MOPSO dengan nilai sinyal control Wu 0.0397 dan sinyal control error Wdu 0.1780 menghasilkan respon sistem dengan maksimum overshoot = 5% Error Steady State = 3 % settling time 65 detik. Selain itu, dibangun juga control PSO – PID yang digunakan sebagai pembanding dimana mode MOPSO menghasilkan nilai Kp = 3.0828 Ki = 0.4219 memiliki respon sistem dengan maksimum overshoot = 3 % Error Steady State = 2% dan settling time 250 detik. Sedangkan pada mode IMOPS nilai Kp = 2.9388 Ki = 0.2166 memiliki respon system dengan maksimum overshoot = 3 % Error Steady State 1.5% dan settling time 150 detik.
Optimal obstacle control problem
Institute of Scientific and Technical Information of China (English)
ZHU Li; LI Xiu-hua; GUO Xing-ming
2008-01-01
In the paper we discuss some properties of the state operators of the optimal obstacle control problem for elliptic variational inequality. Existence, uniqueness and regularity of the optimal control problem are established. In addition, the approximation of the optimal obstacle problem is also studied.
Economic COP Optimization of a Heat Pump with Hierarchical Model Predictive Control
DEFF Research Database (Denmark)
Tahersima, Fatemeh; Stoustrup, Jakob; Rasmussen, Henrik
2012-01-01
A low-temperature heating system is studied in this paper. It consists of hydronic under-floor heating pipes and an air/ground source heat pump. The heat pump in such a setup is conventionally controlled only by feed-forwarding the ambient temperature. Having shown >10% cut-down on electricity...... bills by involving feedback control in a previous study, this paper has continued the same line of argument and has investigated effects of a priori knowledge on weather forecast and electricity price profile to alleviate the total electricity cost subject to constraints on resident's thermal comfort......'s coefficient of performance. At the same time, it determines the actual temperature set-points of the rooms by deviating from the user-defined set-points within a thermal tolerance zone. Simulations results confirm significant cut-down on electricity bills without sacrificing resident thermal comfort...
Directory of Open Access Journals (Sweden)
Robert Pooler
2017-01-01
Full Text Available Low ore-grade waste samples from the Codelco Andina mine that were analyzed in an environmental and mineralogical test program for acid rock drainage prediction, revealed inconsistencies between the quantitative mineralogical data (QEMSCAN® and the results of geochemical characterizations by atomic absorption spectroscopy (AAS, LECO® furnace, and sequential extractions. For the QEMSCAN® results, biases were observed in the proportions of pyrite and calcium sulfate minerals detected. An analysis of the results indicated that the problems observed were likely associated with polished section preparation. Therefore, six different sample preparation protocols were tested and evaluated using three samples from the previous study. One of the methods, which involved particle size reduction and transverse section preparation, was identified as having the greatest potential for correcting the errors observed in the mineralogical analyses. Further, the biases in the quantities of calcium sulfate minerals detected were reduced through the use of ethylene glycol as a polishing lubricant. It is recommended that the sample preparation methodology described in this study be used in order to accurately quantify percentages of pyrite and calcium sulfate minerals in environmental mineralogical studies which use automated mineralogical analysis.
Control and optimization system
Xinsheng, Lou
2013-02-12
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Schaft, A.J. van der
1987-01-01
It is argued that the existence of symmetries may simplify, as in classical mechanics, the solution of optimal control problems. A procedure for obtaining symmetries for the optimal Hamiltonian resulting from the Maximum Principle is given; this avoids the actual calculation of the optimal
Directory of Open Access Journals (Sweden)
Meng Xiong
2015-08-01
Full Text Available Energy storage devices are expected to be more frequently implemented in wind farms in near future. In this paper, both pumped hydro and fly wheel storage systems are used to assist a wind farm to smooth the power fluctuations. Due to the significant difference in the response speeds of the two storages types, the wind farm coordination with two types of energy storage is a problem. This paper presents two methods for the coordination problem: a two-level hierarchical model predictive control (MPC method and a single-level MPC method. In the single-level MPC method, only one MPC controller coordinates the wind farm and the two storage systems to follow the grid scheduling. Alternatively, in the two-level MPC method, two MPC controllers are used to coordinate the wind farm and the two storage systems. The structure of two level MPC consists of outer level and inner level MPC. They run alternatively to perform real-time scheduling and then stop, thus obtaining long-term scheduling results and sending some results to the inner level as input. The single-level MPC method performs both long- and short-term scheduling tasks in each interval. The simulation results show that the methods proposed can improve the utilization of wind power and reduce wind power spillage. In addition, the single-level MPC and the two-level MPC are not interchangeable. The single-level MPC has the advantage of following the grid schedule while the two-level MPC can reduce the optimization time by 60%.
Optimal control computer programs
Kuo, F.
1992-01-01
The solution of the optimal control problem, even with low order dynamical systems, can usually strain the analytical ability of most engineers. The understanding of this subject matter, therefore, would be greatly enhanced if a software package existed that could simulate simple generic problems. Surprisingly, despite a great abundance of commercially available control software, few, if any, address the part of optimal control in its most generic form. The purpose of this paper is, therefore, to present a simple computer program that will perform simulations of optimal control problems that arise from the first necessary condition and the Pontryagin's maximum principle.
Linden, R.D. van der; Leemhuis, A.P.
2010-01-01
Increasingly the upstream oil & gas industry is using active flow control (e.g. feedback loops) or passive flow control (e.g. passive ICD) technologies to optimize asset production. They are used, for example, to commingle production, stabilize production in case of water or gas breakthrough, and to
Neural Network Nonlinear Predictive Control Based on Tent-map Chaos Optimization%基于Tent混沌优化的神经网络预测控制
Institute of Scientific and Technical Information of China (English)
宋莹; 陈增强; 袁著祉
2007-01-01
With the unique ergodicity, irregularity, and special ability to avoid being trapped in local optima, chaos optimization has been a novel global optimization technique and has attracted considerable attention for application in various fields, such as nonlinear programming problems. In this article, a novel neural network nonlinear predictive control (NNPC) strategy based on the new Tent-map chaos optimization algorithm (TCOA) is presented. The feedforward neural network is used as the multi-step predictive model. In addition, the TCOA is applied to perform the nonlinear rolling optimization to enhance the convergence and accuracy in the NNPC. Simulation on a laboratory-scale liquid-level system is given to illustrate the effectiveness of the proposed method.
Optimality principles in sensorimotor control.
Todorov, Emanuel
2004-09-01
The sensorimotor system is a product of evolution, development, learning and adaptation-which work on different time scales to improve behavioral performance. Consequently, many theories of motor function are based on 'optimal performance': they quantify task goals as cost functions, and apply the sophisticated tools of optimal control theory to obtain detailed behavioral predictions. The resulting models, although not without limitations, have explained more empirical phenomena than any other class. Traditional emphasis has been on optimizing desired movement trajectories while ignoring sensory feedback. Recent work has redefined optimality in terms of feedback control laws, and focused on the mechanisms that generate behavior online. This approach has allowed researchers to fit previously unrelated concepts and observations into what may become a unified theoretical framework for interpreting motor function. At the heart of the framework is the relationship between high-level goals, and the real-time sensorimotor control strategies most suitable for accomplishing those goals.
Directory of Open Access Journals (Sweden)
Douglas Halamay
2014-09-01
Full Text Available This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps, can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.
Colonius, Fritz
1988-01-01
This research monograph deals with optimal periodic control problems for systems governed by ordinary and functional differential equations of retarded type. Particular attention is given to the problem of local properness, i.e. whether system performance can be improved by introducing periodic motions. Using either Ekeland's Variational Principle or optimization theory in Banach spaces, necessary optimality conditions are proved. In particular, complete proofs of second-order conditions are included and the result is used for various versions of the optimal periodic control problem. Furthermore a scenario for local properness (related to Hopf bifurcation) is drawn up, giving hints as to where to look for optimal periodic solutions. The book provides mathematically rigorous proofs for results which are potentially of importance in chemical engineering and aerospace engineering.
Sunan, Huang; Heng, Lee Tong
2002-01-01
The presence of considerable time delays in the dynamics of many industrial processes, leading to difficult problems in the associated closed-loop control systems, is a well-recognized phenomenon. The performance achievable in conventional feedback control systems can be significantly degraded if an industrial process has a relatively large time delay compared with the dominant time constant. Under these circumstances, advanced predictive control is necessary to improve the performance of the control system significantly. The book is a focused treatment of the subject matter, including the fundamentals and some state-of-the-art developments in the field of predictive control. Three main schemes for advanced predictive control are addressed in this book: • Smith Predictive Control; • Generalised Predictive Control; • a form of predictive control based on Finite Spectrum Assignment. A substantial part of the book addresses application issues in predictive control, providing several interesting case studie...
Adaptive filtering prediction and control
Goodwin, Graham C
2009-01-01
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Discrete Variational Optimal Control
Jimenez, Fernando; de Diego, David Martin
2012-01-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher-dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical and a practical examples, e.g. the control of an underwater vehicle, will illustrate the application of the proposed approach.
Discrete Variational Optimal Control
Jiménez, Fernando; Kobilarov, Marin; Martín de Diego, David
2013-06-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, and underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical examples and a practical one, the control of an underwater vehicle, illustrate the application of the proposed approach.
Interaction Prediction Optimization in Multidisciplinary Design Optimization Problems
Directory of Open Access Journals (Sweden)
Debiao Meng
2014-01-01
Full Text Available The distributed strategy of Collaborative Optimization (CO is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM, is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.
On Symmetries in Optimal Control
van der Schaft, A. J.
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
On Symmetries in Optimal Control
Schaft, A.J. van der
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
Optimized joystick controller.
Ding, D; Cooper, R A; Spaeth, D
2004-01-01
The purpose of the study was to develop an optimized joystick control interface for electric powered wheelchairs and thus provide safe and effective control of electric powered wheelchairs to people with severe physical disabilities. The interface enables clinicians to tune joystick parameters for each individual subject through selecting templates, dead zones, and bias axes. In terms of hand tremor usually associated with people with traumatic brain injury, cerebral palsy, and multiple sclerosis, fuzzy logic rules were applied to suppress erratic hand movements and extract the intended motion from the joystick. Simulation results were presented to show the graphical tuning interface as well as the performance of the fuzzy logic controller.
Dynamic Algorithm for LQGPC Predictive Control
DEFF Research Database (Denmark)
Hangstrup, M.; Ordys, A.W.; Grimble, M.J.
1998-01-01
In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control...
Optimal control for chemical engineers
Upreti, Simant Ranjan
2013-01-01
Optimal Control for Chemical Engineers gives a detailed treatment of optimal control theory that enables readers to formulate and solve optimal control problems. With a strong emphasis on problem solving, the book provides all the necessary mathematical analyses and derivations of important results, including multiplier theorems and Pontryagin's principle.The text begins by introducing various examples of optimal control, such as batch distillation and chemotherapy, and the basic concepts of optimal control, including functionals and differentials. It then analyzes the notion of optimality, de
Manning, Robert M.
1990-01-01
A static and dynamic rain-attenuation model is presented which describes the statistics of attenuation on an arbitrarily specified satellite link for any location for which there are long-term rainfall statistics. The model may be used in the design of the optimal stochastic control algorithms to mitigate the effects of attenuation and maintain link reliability. A rain-statistics data base is compiled, which makes it possible to apply the model to any location in the continental U.S. with a resolution of 0-5 degrees in latitude and longitude. The model predictions are compared with experimental observations, showing good agreement.
Power, control and optimization
Vasant, Pandian; Barsoum, Nader
2013-01-01
The book consists of chapters based on selected papers of international conference „Power, Control and Optimization 2012”, held in Las Vegas, USA. Readers can find interesting chapters discussing various topics from the field of power control, its distribution and related fields. Book discusses topics like energy consumption impacted by climate, mathematical modeling of the influence of thermal power plant on the aquatic environment, investigation of cost reduction in residential electricity bill using electric vehicle at peak times or allocation and size evaluation of distributed generation using ANN model and others. Chapter authors are to the best of our knowledge the originators or closely related to the originators of presented ideas and its applications. Hence, this book certainly is one of the few books discussing the benefit from intersection of those modern and fruitful scientific fields of research with very tight and deep impact on real life and industry. This book is devoted to the studies o...
Oil Reservoir Production Optimization using Optimal Control
DEFF Research Database (Denmark)
Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan
2011-01-01
Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...... the adjoint method. We use an Explicit Singly Diagonally Implicit Runge-Kutta (ESDIRK) method for the integration and a quasi-Newton Sequential Quadratic Programming (SQP) algorithm for the constrained optimization. We use this algorithm in a numerical case study to optimize the production of oil from an oil...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%....
Optimal Control of Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
Optimal Control of Evolutionary Dynamics
Chakrabarti, Raj; McLendon, George
2008-01-01
Elucidating the fitness measures optimized during the evolution of complex biological systems is a major challenge in evolutionary theory. We present experimental evidence and an analytical framework demonstrating how biochemical networks exploit optimal control strategies in their evolutionary dynamics. Optimal control theory explains a striking pattern of extremization in the redox potentials of electron transport proteins, assuming only that their fitness measure is a control objective functional with bounded controls.
Optimal Control of Mechanical Systems
Vadim Azhmyakov
2007-01-01
In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some ...
MODEL PREDICTIVE CONTROL FUNDAMENTALS
African Journals Online (AJOL)
2012-07-02
Jul 2, 2012 ... paper, we will present an introduction to the theory and application of MPC with Matlab codes written to ... model predictive control, linear systems, discrete-time systems, ... and then compute very rapidly for this open-loop con-.
Enhanced Ocean Predictability Through Optimal Observing Strategies
2016-06-14
Enhanced Ocean Predictability Through Optimal Observing Strategies A. D. Kirwan, Jr. College of Marine Studies University of Delaware Robinson Hall...observation strategies that will maximize the capacity to predict mesoscale and submesoscale conditions so as to provide the best possible nowcasts and...systems approaches on developing optimal observing strategies . The common thread linking both approaches is Lagrangian data, so this phase of the work
NONLINEAR PREDICTIVE CONTROL FOR TERRAIN FOLLOWING
Institute of Scientific and Technical Information of China (English)
1998-01-01
A nonlinear continuous predictive control method was used for design of cruise missile terrain-following controller. A performance index which combined the tracking error and rate of tracking error is presented. Then an optimal nonlinear feedback control law is generated to minimize the performance index. The tracking performance and robustness of controller are discussed. The advantage of the control law is demonstrated by successfully designing cruise missile terrain following controllers. The results show that the controller exhibits robustness and excellent tracking performance.
Hybrid Predictive Control for Dynamic Transport Problems
Núñez, Alfredo A; Cortés, Cristián E
2013-01-01
Hybrid Predictive Control for Dynamic Transport Problems develops methods for the design of predictive control strategies for nonlinear-dynamic hybrid discrete-/continuous-variable systems. The methodology is designed for real-time applications, particularly the study of dynamic transport systems. Operational and service policies are considered, as well as cost reduction. The control structure is based on a sound definition of the key variables and their evolution. A flexible objective function able to capture the predictive behaviour of the system variables is described. Coupled with efficient algorithms, mainly drawn from the area of computational intelligence, this is shown to optimize performance indices for real-time applications. The framework of the proposed predictive control methodology is generic and, being able to solve nonlinear mixed-integer optimization problems dynamically, is readily extendable to other industrial processes. The main topics of this book are: ●hybrid predictive control (HPC) ...
Optimal design criteria - prediction vs. parameter estimation
Waldl, Helmut
2014-05-01
G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.
Optimal magnetic attitude control
DEFF Research Database (Denmark)
Wisniewski, Rafal; Markley, F.L.
1999-01-01
because control torques can only be generated perpendicular to the local geomagnetic field vector. This has been a serious obstacle for using magnetorquer based control for three-axis stabilization of a low earth orbit satellite. The problem of controlling the spacecraft attitude using only magnetic...
Optimal control studies for steamflooding
Energy Technology Data Exchange (ETDEWEB)
Liu, Wei.
1992-01-01
A system science approach using optimal control theory of distributed parameter systems has been developed to determine operating strategies that maximize the economic attractiveness of the steamflooding Enhanced Oil Recovery (EOR) process. Necessary conditions for optimization are established by using the calculus of variations and Pontryagin's Maximum Principle. The objective criterion is to maximize the difference between oil revenue and injected steam cost. A stable and efficient numerical algorithm, based on an iterative gradient method, is developed. The optimal control model is based on a three-dimensional, three-phase (oil, steam and water) steam injection numerical simulator. A discrete form of the model is formulated. The optimized operating variables are the optimal bottom-hole pressure, the optimal injection rate of steam and water, and the optimal steam quality policies. Another optimal control study is also conducted on a simplified one-dimensional model (the extended Neuman model) to provide quick and reliable preliminary information on the economic feasibility of steamflooding processes. The simplified control model only considers the injection rate of steam as the control variable. The performance of this system science approach is investigated through various one-, two- and three-dimensional steamflooding problems. The effects of reservoir properties and heterogeneity on optimal policies as well as the sensitivity of the control variables are also studied. Results show this approach yields significant insight into the steamflooding EOR process. Improvement of the economic objective is significant under optimal operation conditions. These optimization results are quite important in a successful application of the steamflooding EOR method.
1979-12-01
with Uncertain Components 44 13 Component Uncertainty Representation of Uncertain Pole-Zero Locations 46 12 A Feedback Control System 60 i 1 I vii €in...OF FEEDBACK SYSTEM ROBUSTNESS A feedback control system design is said to be robust if it is able to meet design specifications despite differences... feedback control system design problems, the design specifications usually demand that the system be "robust" against the effects of deviations within
Optimal control in thermal engineering
Badescu, Viorel
2017-01-01
This book is the first major work covering applications in thermal engineering and offering a comprehensive introduction to optimal control theory, which has applications in mechanical engineering, particularly aircraft and missile trajectory optimization. The book is organized in three parts: The first part includes a brief presentation of function optimization and variational calculus, while the second part presents a summary of the optimal control theory. Lastly, the third part describes several applications of optimal control theory in solving various thermal engineering problems. These applications are grouped in four sections: heat transfer and thermal energy storage, solar thermal engineering, heat engines and lubrication.Clearly presented and easy-to-use, it is a valuable resource for thermal engineers and thermal-system designers as well as postgraduate students.
Optimal control novel directions and applications
Aronna, Maria; Kalise, Dante
2017-01-01
Focusing on applications to science and engineering, this book presents the results of the ITN-FP7 SADCO network’s innovative research in optimization and control in the following interconnected topics: optimality conditions in optimal control, dynamic programming approaches to optimal feedback synthesis and reachability analysis, and computational developments in model predictive control. The novelty of the book resides in the fact that it has been developed by early career researchers, providing a good balance between clarity and scientific rigor. Each chapter features an introduction addressed to PhD students and some original contributions aimed at specialist researchers. Requiring only a graduate mathematical background, the book is self-contained. It will be of particular interest to graduate and advanced undergraduate students, industrial practitioners and to senior scientists wishing to update their knowledge.
Symposium on Optimal Control Theory
1987-01-01
Control theory can be roughly classified as deterministic or stochastic. Each of these can further be subdivided into game theory and optimal control theory. The central problem of control theory is the so called constrained maximization (which- with slight modifications--is equivalent to minimization). One can then say, heuristically, that the major problem of control theory is to find the maximum of some performance criterion (or criteria), given a set of constraints. The starting point is, of course, a mathematical representation of the performance criterion (or criteria)- sometimes called the objective functional--along with the constraints. When the objective functional is single valued (Le. , when there is only one objective to be maximized), then one is dealing with optimal control theory. When more than one objective is involved, and the objectives are generally incompatible, then one is dealing with game theory. The first paper deals with stochastic optimal control, using the dynamic programming ...
Networked and Distributed Predictive Control
Christofides, Panagiotis D; De La Pena, David Munoz
2011-01-01
"Networked and Distributed Predictive Control" presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems - the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to main
Optimal control theory an introduction
Kirk, Donald E
2004-01-01
Optimal control theory is the science of maximizing the returns from and minimizing the costs of the operation of physical, social, and economic processes. Geared toward upper-level undergraduates, this text introduces three aspects of optimal control theory: dynamic programming, Pontryagin's minimum principle, and numerical techniques for trajectory optimization.Chapters 1 and 2 focus on describing systems and evaluating their performances. Chapter 3 deals with dynamic programming. The calculus of variations and Pontryagin's minimum principle are the subjects of chapters 4 and 5, and chapter
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
Model predictive control classical, robust and stochastic
Kouvaritakis, Basil
2016-01-01
For the first time, a textbook that brings together classical predictive control with treatment of up-to-date robust and stochastic techniques. Model Predictive Control describes the development of tractable algorithms for uncertain, stochastic, constrained systems. The starting point is classical predictive control and the appropriate formulation of performance objectives and constraints to provide guarantees of closed-loop stability and performance. Moving on to robust predictive control, the text explains how similar guarantees may be obtained for cases in which the model describing the system dynamics is subject to additive disturbances and parametric uncertainties. Open- and closed-loop optimization are considered and the state of the art in computationally tractable methods based on uncertainty tubes presented for systems with additive model uncertainty. Finally, the tube framework is also applied to model predictive control problems involving hard or probabilistic constraints for the cases of multiplic...
Optimal actuation in vibration control
Guzzardo, C. A.; Pang, S. S.; Ram, Y. M.
2013-02-01
The paper addresses the problem of finding the optimal location of actuators and their relative gain so that the control effort in an actively controlled vibrating system is minimized. In technical terms the problem is finding the optimal input vector of unit norm that minimizes the norm of the control gain vector. This problem is addressed in the context of the active natural frequency modification problem associated with resonance avoidance in undamped systems, and in the context of the single-input-multi-output pole assignment problem for second order systems.
Optimal Control of Teaching Process
Institute of Scientific and Technical Information of China (English)
BAO Man; ZHANG Guo-zhi
2002-01-01
The authors first put forward quadratic form performance index as a criterion of measuringmerits and demerits of teaching process. On this base, we got a low of optimal control after the quantificationof the teacher's functions. It must play a leading role on how the teacher fully controls the whole teachingprocess.
Optimal control of quantum measurement
Energy Technology Data Exchange (ETDEWEB)
Egger, Daniel; Wilhelm, Frank [Theoretical Physics, Saarland University, 66123 Saarbruecken (Germany)
2015-07-01
Pulses to steer the time evolution of quantum systems can be designed with optimal control theory. In most cases it is the coherent processes that can be controlled and one optimizes the time evolution towards a target unitary process, sometimes also in the presence of non-controllable incoherent processes. Here we show how to extend the GRAPE algorithm in the case where the incoherent processes are controllable and the target time evolution is a non-unitary quantum channel. We perform a gradient search on a fidelity measure based on Choi matrices. We illustrate our algorithm by optimizing a measurement pulse for superconducting phase qubits. We show how this technique can lead to large measurement contrast close to 99%. We also show, within the validity of our model, that this algorithm can produce short 1.4 ns pulses with 98.2% contrast.
Optimality Conditions for Inventory Control
Feinberg, Eugene A.
2016-01-01
This tutorial describes recently developed general optimality conditions for Markov Decision Processes that have significant applications to inventory control. In particular, these conditions imply the validity of optimality equations and inequalities. They also imply the convergence of value iteration algorithms. For total discounted-cost problems only two mild conditions on the continuity of transition probabilities and lower semi-continuity of one-step costs are needed. For average-cost pr...
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
Directory of Open Access Journals (Sweden)
Felix Jost
2017-02-01
Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.
Optimal prediction intervals of wind power generation
DEFF Research Database (Denmark)
Wan, Can; Wu, Zhao; Pinson, Pierre
2014-01-01
Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind...... penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with Associated confidence levels are generated through...... conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility...
Optimality principles for model-based prediction of human gait.
Ackermann, Marko; van den Bogert, Antonie J
2010-04-19
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.
Optimal prediction intervals of wind power generation
Wan, Can; Wu, Zhao; Pinson, Pierre; Dong, Zhao Yang; Wong, Kit Po
2014-01-01
Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machin...
Optimal control linear quadratic methods
Anderson, Brian D O
2007-01-01
This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the
Optimal control of motorsport differentials
Tremlett, A. J.; Massaro, M.; Purdy, D. J.; Velenis, E.; Assadian, F.; Moore, A. P.; Halley, M.
2015-12-01
Modern motorsport limited slip differentials (LSD) have evolved to become highly adjustable, allowing the torque bias that they generate to be tuned in the corner entry, apex and corner exit phases of typical on-track manoeuvres. The task of finding the optimal torque bias profile under such varied vehicle conditions is complex. This paper presents a nonlinear optimal control method which is used to find the minimum time optimal torque bias profile through a lane change manoeuvre. The results are compared to traditional open and fully locked differential strategies, in addition to considering related vehicle stability and agility metrics. An investigation into how the optimal torque bias profile changes with reduced track-tyre friction is also included in the analysis. The optimal LSD profile was shown to give a performance gain over its locked differential counterpart in key areas of the manoeuvre where a quick direction change is required. The methodology proposed can be used to find both optimal passive LSD characteristics and as the basis of a semi-active LSD control algorithm.
Control and optimal control theories with applications
Burghes, D N
2004-01-01
This sound introduction to classical and modern control theory concentrates on fundamental concepts. Employing the minimum of mathematical elaboration, it investigates the many applications of control theory to varied and important present-day problems, e.g. economic growth, resource depletion, disease epidemics, exploited population, and rocket trajectories. An original feature is the amount of space devoted to the important and fascinating subject of optimal control. The work is divided into two parts. Part one deals with the control of linear time-continuous systems, using both transfer fun
Optimal control with aerospace applications
Longuski, James M; Prussing, John E
2014-01-01
Want to know not just what makes rockets go up but how to do it optimally? Optimal control theory has become such an important field in aerospace engineering that no graduate student or practicing engineer can afford to be without a working knowledge of it. This is the first book that begins from scratch to teach the reader the basic principles of the calculus of variations, develop the necessary conditions step-by-step, and introduce the elementary computational techniques of optimal control. This book, with problems and an online solution manual, provides the graduate-level reader with enough introductory knowledge so that he or she can not only read the literature and study the next level textbook but can also apply the theory to find optimal solutions in practice. No more is needed than the usual background of an undergraduate engineering, science, or mathematics program: namely calculus, differential equations, and numerical integration. Although finding optimal solutions for these problems is a...
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired.
Optimal control of hybrid vehicles
Jager, Bram; Kessels, John
2013-01-01
Optimal Control of Hybrid Vehicles provides a description of power train control for hybrid vehicles. The background, environmental motivation and control challenges associated with hybrid vehicles are introduced. The text includes mathematical models for all relevant components in the hybrid power train. The power split problem in hybrid power trains is formally described and several numerical solutions detailed, including dynamic programming and a novel solution for state-constrained optimal control problems based on Pontryagin’s maximum principle. Real-time-implementable strategies that can approximate the optimal solution closely are dealt with in depth. Several approaches are discussed and compared, including a state-of-the-art strategy which is adaptive for vehicle conditions like velocity and mass. Two case studies are included in the book: · a control strategy for a micro-hybrid power train; and · experimental results obtained with a real-time strategy implemented in...
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Optimal control of hydroelectric facilities
Zhao, Guangzhi
This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the
Application of optimal prediction to molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Barber, IV, John Letherman [Univ. of California, Berkeley, CA (United States)
2004-12-01
Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is δ-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.
Optimal Control of Electrodynamic Tethers
2008-06-01
left with ( ) ( ) 1 2 1 2 23 3 3 32 1 2 1 2 3 3 ˆ ˆ 2 2 2 ˆ ˆ 6 6 t t t t t t m m m m m T m L m L M M m LM M M MLm M M... Contract RH4-394049, March 1985, p 31. 9 Pelaez, J. and Lorenzini, E. C., “Libration Control of Electrodynamic Tethers in Inclined Orbit,” Journal of...COVERED (From – To) Aug 2006 – Jul 2008 4. TITLE AND SUBTITLE Optimal Control of Electrodynamic Tethers 5a. CONTRACT NUMBER 5b
Modelling, controlling, predicting blackouts
Wang, Chengwei; Baptista, Murilo S
2016-01-01
The electric power system is one of the cornerstones of modern society. One of its most serious malfunctions is the blackout, a catastrophic event that may disrupt a substantial portion of the system, playing havoc to human life and causing great economic losses. Thus, understanding the mechanisms leading to blackouts and creating a reliable and resilient power grid has been a major issue, attracting the attention of scientists, engineers and stakeholders. In this paper, we study the blackout problem in power grids by considering a practical phase-oscillator model. This model allows one to simultaneously consider different types of power sources (e.g., traditional AC power plants and renewable power sources connected by DC/AC inverters) and different types of loads (e.g., consumers connected to distribution networks and consumers directly connected to power plants). We propose two new control strategies based on our model, one for traditional power grids, and another one for smart grids. The control strategie...
Predictive Terminal Guidance With Tuning of Prediction Horizon & Constrained Control .
Directory of Open Access Journals (Sweden)
S. E. Talole
2000-07-01
Full Text Available Continvojs time-predictive control approach is employed to formulate an output tracking nonlinear, optimal, terminal guidance ,law for re-entry vehicles. The notable features of this formulation are that the system equations are not linearised and the evaluation of the guidanceequations does not need the information of vehicle parameters, such as drag and mass. The formulation allows to impose the physical constrains on the control inputs, i..e. on the demanded lateral acceleliations through a saturation mapping and the controls are obtained using a fixed pointiteration algorithm which converges typically in a few iterations. Further, a simple method of tuning the prediction horizon needed in the guidance equations is presented. Numerical simulations show that the guidance law achieves almost zero terminal errors in all states despite large errors in initial Conditions.
Induced optimism as mental rehearsal to decrease depressive predictive certainty.
Miranda, Regina; Weierich, Mariann; Khait, Valerie; Jurska, Justyna; Andersen, Susan M
2017-03-01
The present study examined whether practice in making optimistic future-event predictions would result in change in the hopelessness-related cognitions that characterize depression. Individuals (N = 170) with low, mild, and moderate-to-severe depressive symptoms were randomly assigned to a condition in which they practiced making optimistic future-event predictions or to a control condition in which they viewed the same stimuli but practiced determining whether a given phrase contained an adjective. Overall, individuals in the induced optimism condition showed increases in optimistic predictions, relative to the control condition, as a result of practice, but only individuals with moderate-to-severe symptoms of depression who practiced making optimistic future-event predictions showed decreases in depressive predictive certainty, relative to the control condition. In addition, they showed gains in efficiency in making optimistic predictions over the practice blocks, as assessed by response time. There was no difference in depressed mood by practice condition. Mental rehearsal might be one way of changing the hopelessness-related cognitions that characterize depression. Copyright © 2016 Elsevier Ltd. All rights reserved.
HCCI Engine Optimization and Control
Energy Technology Data Exchange (ETDEWEB)
Rolf D. Reitz
2005-09-30
The goal of this project was to develop methods to optimize and control Homogeneous-Charge Compression Ignition (HCCI) engines, with emphasis on diesel-fueled engines. HCCI offers the potential of nearly eliminating IC engine NOx and particulate emissions at reduced cost over Compression Ignition Direct Injection engines (CIDI) by controlling pollutant emissions in-cylinder. The project was initiated in January, 2002, and the present report is the final report for work conducted on the project through December 31, 2004. Periodic progress has also been reported at bi-annual working group meetings held at USCAR, Detroit, MI, and at the Sandia National Laboratories. Copies of these presentation materials are available on CD-ROM, as distributed by the Sandia National Labs. In addition, progress has been documented in DOE Advanced Combustion Engine R&D Annual Progress Reports for FY 2002, 2003 and 2004. These reports are included as the Appendices in this Final report.
Power optimized programmable embedded controller
Kamaraju, M; Tilak, A V N; 10.5121/ijcnc.2010.2409
2010-01-01
Now a days, power has become a primary consideration in hardware design, and is critical in computer systems especially for portable devices with high performance and more functionality. Clock-gating is the most common technique used for reducing processor's power. In this work clock gating technique is applied to optimize the power of fully programmable Embedded Controller (PEC) employing RISC architecture. The CPU designed supports i) smart instruction set, ii) I/O port, UART iii) on-chip clocking to provide a range of frequencies , iv) RISC as well as controller concepts. The whole design is captured using VHDL and is implemented on FPGA chip using Xilinx .The architecture and clock gating technique together is found to reduce the power consumption by 33.33% of total power consumed by this chip.
Gormley, Andrew M; Holland, E Penelope; Barron, Mandy C; Anderson, Dean P; Nugent, Graham
2016-03-01
Bovine tuberculosis (TB) impacts livestock farming in New Zealand, where the introduced marsupial brushtail possum (Trichosurus vulpecula) is the wildlife maintenance host for Mycobacterium bovis. New Zealand has implemented a campaign to control TB using a co-ordinated programme of livestock diagnostic testing and large-scale culling of possums, with the long-term aim of TB eradication. For management of the disease in wildlife, methods that can optimise the balance between control and surveillance effort will facilitate the objective of eradication on a fixed or limited budget. We modelled and compared management options to optimise the balance between the two activities necessary to achieve and verify eradication of TB from New Zealand wildlife: the number of lethal population control operations required to halt the M. bovis infection cycle in possums, and the subsequent surveillance effort needed to confidently declare TB freedom post-control. The approach considered the costs of control and surveillance, as well as the potential costs of re-control resulting from false declaration of TB freedom. The required years of surveillance decreased with increasing numbers of possum lethal control operations but the overall time to declare TB freedom depended on additional factors, such as the probability of freedom from disease after control and the probability of success of mop-up control, i.e. retroactive culling following detection of persistent disease in the residual possum population. The total expected cost was also dependent on a number of factors, many of which had wide cost ranges, suggesting that an optimal strategy is unlikely to be singular and fixed, but will likely vary for each different area being considered. Our approach provides a simple framework that considers the known and potential costs of possum control and TB surveillance, enabling managers to optimise the balance between these two activities to achieve and prove eradication of a wildlife
Predictability and unpredictability in optical system optimization
Van Turnhout, M.; Bociort, F.
2007-01-01
Local optimization algorithms, when they are optimized only for speed, have in certain situations an unpredictable behavior: starting points very close to each other lead after optimization to different minima. In these cases, the sets of points, which, when chosen as starting points for local optim
Predictability and unpredictability in optical system optimization
Van Turnhout, M.; Bociort, F.
2007-01-01
Local optimization algorithms, when they are optimized only for speed, have in certain situations an unpredictable behavior: starting points very close to each other lead after optimization to different minima. In these cases, the sets of points, which, when chosen as starting points for local optim
Optimal control of induction heating processes
Rapoport, Edgar
2006-01-01
This book introduces new approaches to solving optimal control problems in induction heating process applications. Optimal Control of Induction Heating Processes demonstrates how to apply and use new optimization techniques for different types of induction heating installations. Focusing on practical methods for solving real engineering optimization problems, the text features a variety of specific optimization examples for induction heater modes and designs, particularly those used in industrial applications. The book describes basic physical phenomena in induction heating and induction
Aerodynamic shape optimization using control theory
Reuther, James
1996-01-01
Aerodynamic shape design has long persisted as a difficult scientific challenge due its highly nonlinear flow physics and daunting geometric complexity. However, with the emergence of Computational Fluid Dynamics (CFD) it has become possible to make accurate predictions of flows which are not dominated by viscous effects. It is thus worthwhile to explore the extension of CFD methods for flow analysis to the treatment of aerodynamic shape design. Two new aerodynamic shape design methods are developed which combine existing CFD technology, optimal control theory, and numerical optimization techniques. Flow analysis methods for the potential flow equation and the Euler equations form the basis of the two respective design methods. In each case, optimal control theory is used to derive the adjoint differential equations, the solution of which provides the necessary gradient information to a numerical optimization method much more efficiently then by conventional finite differencing. Each technique uses a quasi-Newton numerical optimization algorithm to drive an aerodynamic objective function toward a minimum. An analytic grid perturbation method is developed to modify body fitted meshes to accommodate shape changes during the design process. Both Hicks-Henne perturbation functions and B-spline control points are explored as suitable design variables. The new methods prove to be computationally efficient and robust, and can be used for practical airfoil design including geometric and aerodynamic constraints. Objective functions are chosen to allow both inverse design to a target pressure distribution and wave drag minimization. Several design cases are presented for each method illustrating its practicality and efficiency. These include non-lifting and lifting airfoils operating at both subsonic and transonic conditions.
Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda
2017-01-01
Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results. PMID:28125609
Jaddi, Najmeh Sadat; Abdullah, Salwani; Abdul Malek, Marlinda
2017-01-01
Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.
Developments in model-based optimization and control distributed control and industrial applications
Grancharova, Alexandra; Pereira, Fernando
2015-01-01
This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...
Optimal control of sun tracking solar concentrators
Hughes, R. O.
1979-01-01
Application of the modern control theory to derive an optimal sun tracking control for a point focusing solar concentrator is presented. A standard tracking problem converted to regulator problem using a sun rate input achieves an almost zero steady state tracking error with the optimal control formulation. However, these control techniques are costly because optimal type algorithms require large computing systems, thus they will be used mainly as comparison standards for other types of control algorithms and help in their development.
Temperature controller optimization by computational intelligence
Directory of Open Access Journals (Sweden)
Ćojbašić Žarko M.
2016-01-01
Full Text Available In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several metaheuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta-heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency. [Projekat Ministarstva nauke Republike Srbije, br. TR 33047 i br. TR 35016
Constrained Optimization and Optimal Control for Partial Differential Equations
Leugering, Günter; Griewank, Andreas
2012-01-01
This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in this emerging field. Indeed, even though the field of optimal control and optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear problems is still lacking. The cont
NONLINEAR MODEL PREDICTIVE CONTROL OF CHEMICAL PROCESSES
Directory of Open Access Journals (Sweden)
R. G. SILVA
1999-03-01
Full Text Available A new algorithm for model predictive control is presented. The algorithm utilizes a simultaneous solution and optimization strategy to solve the model's differential equations. The equations are discretized by equidistant collocation, and along with the algebraic model equations are included as constraints in a nonlinear programming (NLP problem. This algorithm is compared with the algorithm that uses orthogonal collocation on finite elements. The equidistant collocation algorithm results in simpler equations, providing a decrease in computation time for the control moves. Simulation results are presented and show a satisfactory performance of this algorithm.
Optimal control of photoelectron emission by realistic waveforms
Solanpää, Janne; Räsänen, Esa
2016-01-01
Recent experimental techniques in multicolor waveform synthesis allow the temporal shaping of strong femtosecond laser pulses with applications in the control of quantum mechanical processes in atoms, molecules, and nanostructures. Prediction of the shapes of the optimal waveforms can be done computationally using quantum optimal control theory (QOCT). In this work we bring QOCT to experimental feasibility by providing an optimal control scheme with realistic pulse representation. We apply the technique to optimal control of above-threshold photoelectron emission from a one-dimensional hydrogen atom. By mixing different spectral channels and thus lowering the intensity requirements for individual channels, the resulting optimal pulses can extend the cutoff energies by at least up to 50% and bring up the electron yield by several orders of magnitude. Insights into the electron dynamics for optimized photoelectron emission are obtained with a semiclassical two-step model.
Fuzzy logic control and optimization system
Lou, Xinsheng [West Hartford, CT
2012-04-17
A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
AN APPLICATION OF OPTIMAL CONTROL THEORY.
The purpose of this article is to show that optimal control theory can be used to develop a control strategy for a practical system, namely a distillation column. The approach will be to model the complex system with a simple model, use optimal control theory to determine a control strategy for the simple model, and then apply the results to the original system. (Author)
A Quasi Time Optimal Receding Horizon Control
Bania, Piotr
2007-01-01
This paper presents a quasi time optimal receding horizon control algorithm. The proposed algorithm generates near time optimal control when the state of the system is far from the target. When the state attains a certain neighbourhood of the aim, it begins the adaptation of the cost function. The purpose of this adaptation is to move from the time optimal control to the stabilizing control. Sufficient conditions for the stability of the closed loop system and the manner of the adaptation of ...
Controlling automobile thermal comfort using optimized fuzzy controller
Energy Technology Data Exchange (ETDEWEB)
Farzaneh, Yadollah; Tootoonchi, Ali A. [Department of Mechanical Engineering, Ferdowsi University of Mashhad, Mashhad (Iran)
2008-10-15
Providing thermal comfort and saving energy are two main goals of heating, ventilation and air conditioning (HVAC) systems. A controller with temperature feedback cannot best achieve the thermal comfort. This is because thermal comfort is influenced by many variables such as, temperature, relative humidity, air velocity, environment radiation, activity level and cloths insulation. In this study Fanger's predicted mean value (PMV) index is used as controller feedback. It is simplified without introducing significant error. Thermal models of the cabin and HVAC system are developed. Evaporator cooling capacity is selected as a criterion for energy consumption. Two fuzzy controllers one with temperature as its feedback and the other PMV index as its feedback are designed. Results show that the PMV feedback controller better controls the thermal comfort and energy consumption than the system with temperature feedback. Next, the parameters of the fuzzy controller are optimized by genetic algorithm. Results indicate that thermal comfort level is further increased while energy consumption is decreased. Finally, robustness analysis is performed which shows the robustness of optimized controller to variables variations. (author)
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Wind turbine control and model predictive control for uncertain systems
DEFF Research Database (Denmark)
Thomsen, Sven Creutz
as disturbance models for controller design. The theoretical study deals with Model Predictive Control (MPC). MPC is an optimal control method which is characterized by the use of a receding prediction horizon. MPC has risen in popularity due to its inherent ability to systematically account for time......-domain constraints on signals. During the last decades several theoretical advances have been made, so that it can handle a wide variety of system structures. In this thesis, the focus is on handling uncertain linear system description. To this end the so-called Youla parameterizations have been used. Two methods...... are proposed: The first method exploits the modularity of the parameterizations so that the uncertainty can be identified and the MPC controller can be reconfigured in a modular setting. The second method is a robust MPC method in which the Youla parameters are used as an integral part of the online...
Optimization of maintenance for power system equipment using a predictive health model
Bajracharya, G.; Koltunowicz, T.; Negenborn, R.R.; Papp, Z.; Djairam, D.; De Schutter, B.; Smit, J.J.
2009-01-01
In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based
A Controlled Particle Filter for Global Optimization
Zhang, Chi; Taghvaei, Amirhossein; Mehta, Prashant G.
2017-01-01
A particle filter is introduced to numerically approximate a solution of the global optimization problem. The theoretical significance of this work comes from its variational aspects: (i) the proposed particle filter is a controlled interacting particle system where the control input represents the solution of a mean-field type optimal control problem; and (ii) the associated density transport is shown to be a gradient flow (steepest descent) for the optimal value function, with respect to th...
Institute of Scientific and Technical Information of China (English)
樊兆峰; 马小平; 邵晓根
2014-01-01
To deal with the performance degradation caused by improper initial values in neural network local optimiza-tion predictive control, we propose a method to dynamically determine the initial values. In each optimization cycle the minimum output error point is selected by calculating the inverse neural network. The existence of the minimal value of the objective function between this point and the current control point can be ensured and proved through modifying the weighting factor. Finally, a simulation experiment is carried out to verify the proposed method using a back propagation (BP) neural network as the predictive model, and the Newton-Raphson algorithm is employed as the receding horizon op-timization strategy. The results show that the initial value problem can be solved to improve the reliability of the control system.%为解决局部优化算法初值选取不当造成神经网络预测控制性能下降的问题，本文提出了一种动态确定初值的方法。在每次优化时通过逆网络将初值选在输出误差最小点，通过修正目标性能函数中的权重因子来确保初值与当前控制量之间存在极值，并在理论上进行了证明。以BP神经网络预测控制为例，采用牛顿拉夫逊算法实现滚动优化，对所提方法进行了仿真实验，结果表明能够解决初值问题，提高控制系统的可靠性。
OPTIMAL CONTROL OF CNC CUTTING PROCESS
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
The intelligent optimizing method of cutting parameters and the cutting stable districts searching method are set up. The cutting parameters of each cutting pass could be optimized automatically, the cutting chatter is predicted through setting up the dynamic cutting force AR(2) model on-line, the spindle rotation speed is adjusted according to the predicting results so as to ensure the cutting system work in stable district.
Optimizing Dynamical Network Structure for Pinning Control
Orouskhani, Yasin; Jalili, Mahdi; Yu, Xinghuo
2016-04-01
Controlling dynamics of a network from any initial state to a final desired state has many applications in different disciplines from engineering to biology and social sciences. In this work, we optimize the network structure for pinning control. The problem is formulated as four optimization tasks: i) optimizing the locations of driver nodes, ii) optimizing the feedback gains, iii) optimizing simultaneously the locations of driver nodes and feedback gains, and iv) optimizing the connection weights. A newly developed population-based optimization technique (cat swarm optimization) is used as the optimization method. In order to verify the methods, we use both real-world networks, and model scale-free and small-world networks. Extensive simulation results show that the optimal placement of driver nodes significantly outperforms heuristic methods including placing drivers based on various centrality measures (degree, betweenness, closeness and clustering coefficient). The pinning controllability is further improved by optimizing the feedback gains. We also show that one can significantly improve the controllability by optimizing the connection weights.
Adaptive optimization and control using neural networks
Energy Technology Data Exchange (ETDEWEB)
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
1993-10-22
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
A Decomposition Algorithm for Optimal Control of Distributed Energy System
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Standardi, Laura
2013-01-01
In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems...
Optimal Control of Switched Systems based on Bezier Control Points
FatemeGhomanjani; Mohammad HadiFarahi
2012-01-01
This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into ...
Predictive Thermal Control Technology for Stable Telescope
Stahl, H. Philip
Predictive Thermal Control (PTC) project is a multiyear effort to develop, demonstrate, mature towards TRL6, and assess the utility of model based Predictive Thermal Control technology to enable a thermally stable telescope. PTC demonstrates technology maturation by model validation and characterization testing of traceable components in a relevant environment. PTC's efforts are conducted in consultation with the Cosmic Origins Office and NASA Program Analysis Groups. To mature Thermally Stable Telescope technology, PTC has three objectives: • Validate models that predict thermal optical performance of real mirrors and structure based on their designs and constituent material properties, i.e. coefficient of thermal expansion (CTE) distribution, thermal conductivity, thermal mass, etc. • Derive thermal system stability specifications from wavefront stability requirements. • Demonstrate utility of Predictive Thermal Control for achieving thermal stability. To achieve these objectives, PTC has five quantifiable milestones: 1. Develop a high-fidelity model of the AMTD-2 1.5 meter ULE® mirror, including 3D CTE distribution and reflective optical coating, that predicts its optical performance response to steady-state and dynamic thermal gradients under bang/bang and proportional thermal control. 2. Derive specifications for thermal control system as a function of wavefront stability. 3. Design and build a predictive Thermal Control System for a 1.5 meter ULE® mirror using new and existing commercial-off-the-shelf components that sense temperature changes at the 1mK level and actively controls the mirrors thermal environment at the 20mK level. 4. Validate the model by testing a 1.5-m class ULE® mirror in a relevant thermal vacuum environment in the MSFC X-ray and Cryogenic Facility (XRCF) test facility. 5. Use validated model to perform trade studies to optimize thermo-optical performance as a function of mirror design, material selection, mass, etc. PTC advances
Process control and optimization with simple interval calculation method
DEFF Research Database (Denmark)
Pomerantsev, A.; Rodionova, O.; Høskuldsson, Agnar
2006-01-01
the series of expanding PLS/SIC models in order to support the on-line process improvements. This method helps to predict the effect of planned actions on the product quality and thus enables passive quality control. We have also considered an optimization approach that proposes the correcting actions......Methods of process control and optimization are presented and illustrated with a real world example. The optimization methods are based on the PLS block modeling as well as on the simple interval calculation methods of interval prediction and object status classification. It is proposed to employ...... for the quality improvement in the course of production. The latter is an active quality optimization, which takes into account the actual history of the process. The advocate approach is allied to the conventional method of multivariate statistical process control (MSPC) as it also employs the historical process...
Multiplexed Predictive Control of a Large Commercial Turbofan Engine
Richter, hanz; Singaraju, Anil; Litt, Jonathan S.
2008-01-01
Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.
Optimal Control and Optimization of Stochastic Supply Chain Systems
Song, Dong-Ping
2013-01-01
Optimal Control and Optimization of Stochastic Supply Chain Systems examines its subject in the context of the presence of a variety of uncertainties. Numerous examples with intuitive illustrations and tables are provided, to demonstrate the structural characteristics of the optimal control policies in various stochastic supply chains and to show how to make use of these characteristics to construct easy-to-operate sub-optimal policies. In Part I, a general introduction to stochastic supply chain systems is provided. Analytical models for various stochastic supply chain systems are formulated and analysed in Part II. In Part III the structural knowledge of the optimal control policies obtained in Part II is utilized to construct easy-to-operate sub-optimal control policies for various stochastic supply chain systems accordingly. Finally, Part IV discusses the optimisation of threshold-type control policies and their robustness. A key feature of the book is its tying together of ...
Optimal Online Prediction in Adversarial Environments
Bartlett, Peter L.
In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.
Using Chemicals to Optimize Conformance Control in Fractured Reservoirs
Energy Technology Data Exchange (ETDEWEB)
Seright, Randall S.; Liang, Jenn-Tai; Schrader, Richard; Hagstrom II, John; Wang, Ying; Kumar, Ananad; Wavrik, Kathryn
2001-10-29
This report describes work performed during the third and final year of the project, Using Chemicals to Optimize Conformance Control in Fractured Reservoirs. This research project had three objectives. The first objective was to develop a capability to predict and optimize the ability of gels to reduce permeability to water more than that to oil or gas. The second objective was to develop procedures for optimizing blocking agent placement in wells where hydraulic fractures cause channeling problems. The third objective was to develop procedures to optimize blocking agent placement in naturally fractured reservoirs.
Optimization of Temperature Controller for Electric Furnace
Institute of Scientific and Technical Information of China (English)
2000-01-01
Genetic algorithms are based on the principle of natural selection and the optimization of natural generation. We can select the number of the bit strings and mutation rate reasonably, the global optimal solution can be obtained. GAs adopt the binary code as optimizing parameter and this binary code can be used in computer controller easily. This paper studies the application of the GAs to the electric furnace temperature control. When the electric furnace mathematics model varies with the working condition, the parameter of controller can be optimized on line. So the system performance can be improved effectively.
OPTIMAL CONTROL PROBLEM FOR PARABOLIC VARIATIONAL INEQUALITIES
Institute of Scientific and Technical Information of China (English)
汪更生
2001-01-01
This paper deals with the optimal control problems of systems governed by a parabolic variational inequality coupled with a semilinear parabolic differential equations.The maximum principle and some kind of approximate controllability are studied.
Fast Solvers of Fredholm Optimal Control Problems
Institute of Scientific and Technical Information of China (English)
Mario; Borzì
2010-01-01
The formulation of optimal control problems governed by Fredholm integral equations of second kind and an efficient computational framework for solving these control problems is presented. Existence and uniqueness of optimal solutions is proved.A collective Gauss-Seidel scheme and a multigrid scheme are discussed. Optimal computational performance of these iterative schemes is proved by local Fourier analysis and demonstrated by results of numerical experiments.
Almost optimal adaptive LQ control: SISO case
Polderman, Jan W.; Daams, Jasper
2002-01-01
In this paper an almost optimal indirect adaptive controller for input/output dynamical systems is proposed. The control part of the adaptive control scheme is based on a modified LQ control law: by adding a time-varying gain to the certainty equivalent control law the conflict between
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, wh
Distributed Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus Fogtmann; Vandenberghe, Lieven; Poulsen, Niels Kjølstad
2016-01-01
Integration of a large number of flexible consumers in a smart grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a model predictive control framework. To solve the large-scale control problem...
Connections Between Singular Control and Optimal Switching
Guo, Xin; Tomecek, Pascal
2007-01-01
This paper builds a new theoretical connection between singular control of finite variation and optimal switching problems. This correspondence provides a novel method for solving high-dimensional singular control problems, and enables us to extend the theory of reversible investment: sufficient conditions are derived for the existence of optimal controls and for the regularity of value functions. Consequently, our regularity result links singular controls and Dynkin games through sequential ...
Riccati difference equation in optimal control for magnetic bearings
Institute of Scientific and Technical Information of China (English)
ZHANG Li; LIU Kun
2012-01-01
A model predictive optimal control method for magnetically suspended flywheel is presented.In order to suppress the conical whirl of the rotor caused by gyroscopic effect,the synchronization error is added to the traditional quadratic performance index.The target performance index is composed of the translatory error,the synchronization error,and the control output predicted by the discrete-time state model.The optimal controller is obtained by means of iterating a Riccati difference equation (RDE).Stability of the control scheme is investigated through fake algebraic Riccati technique (FART).The robust performance of the controller with respect to control parameters is studied by simulation.Results of the simulation and experiment on a compact magnetically suspended flywheel demonstrate that the proposed controller with consideration of the synchronization error is very effective to suppress the conical whirl caused by gyroscopic effect.
Data-Based Predictive Control with Multirate Prediction Step
Barlow, Jonathan S.
2010-01-01
Data-based predictive control is an emerging control method that stems from Model Predictive Control (MPC). MPC computes current control action based on a prediction of the system output a number of time steps into the future and is generally derived from a known model of the system. Data-based predictive control has the advantage of deriving predictive models and controller gains from input-output data. Thus, a controller can be designed from the outputs of complex simulation code or a physical system where no explicit model exists. If the output data happens to be corrupted by periodic disturbances, the designed controller will also have the built-in ability to reject these disturbances without the need to know them. When data-based predictive control is implemented online, it becomes a version of adaptive control. One challenge of MPC is computational requirements increasing with prediction horizon length. This paper develops a closed-loop dynamic output feedback controller that minimizes a multi-step-ahead receding-horizon cost function with multirate prediction step. One result is a reduced influence of prediction horizon and the number of system outputs on the computational requirements of the controller. Another result is an emphasis on portions of the prediction window that are sampled more frequently. A third result is the ability to include more outputs in the feedback path than in the cost function.
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Neural Networks for Optimal Control
DEFF Research Database (Denmark)
Sørensen, O.
1995-01-01
Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....
Institute of Scientific and Technical Information of China (English)
刘普; 王跃; 丛武龙; 雷万钧
2014-01-01
模块化多电平换流器(modular multilevel converter， MMC)具有效率高、谐波小、模块化设计、易级联等优点，在高压大容量电能变换领域得到了日益广泛的应用。作为一种先进的控制策略，模型预测控制(model predictive control， MPC)通过目标函数可同时控制多个系统变量，具有建模直观、动态响应快等优点。传统MMC模型预测控制通过计算所有开关状态组合以实现最优控制目标，但随着桥臂模块数量的增多，计算量将呈几何级数增长，严重制约MPC的工程推广应用。针对N+1电平MMC，提出一种优化的模型预测控制算法，在对子模块电压、交流电流、相间环流、器件开关频率有效控制的同时，将开关状态组合计算量从C2NN降至N+1。针对子模块数高达数百的MMC，进一步提出分组排序优化模型预测控制(grouping-sorting algorithm combined OMPC，GSOMPC)策略，在降低桥臂子模块电压整体排序对硬件资源苛刻需求的同时，将开关状态组合计算量从N+1降至2X+M+3(N=M×X)。基于2.7kV/60kW 23电平MMC背靠背动模实验平台的实验结果证明了所提优化模型预测控制(optimized model predictive control，OMPC)及GSOMPC策略的正确性与有效性。%With the attractive features of high efficiency, low harmonic, modularity and scalability, the Modular Multilevel Converter (MMC) is suitable for a wide range of high-voltage large-capacity applications. As an advanced control strategy, the Model Predictive Control (MPC) can control multiple variables through a cost function, which also has advantages of direct modeling and fast dynamic response performances. The conventional Model Predictive Control method for MMC is applied by calculating all the candidate switching states to achieve control objective optimization, which limits the application of MPC along with the submodule increasing. An Optimized Model Predictive Control (OMPC) was
An Optimal Controller Architecture for Poset-Causal Systems
Shah, Parikshit
2011-01-01
We propose a novel and natural architecture for decentralized control that is applicable whenever the underlying system has the structure of a partially ordered set (poset). This controller architecture is based on the concept of Moebius inversion for posets, and enjoys simple and appealing separation properties, since the closed-loop dynamics can be analyzed in terms of decoupled subsystems. The controller structure provides rich and interesting connections between concepts from order theory such as Moebius inversion and control-theoretic concepts such as state prediction, correction, and separability. In addition, using our earlier results on H_2-optimal decentralized control for arbitrary posets, we prove that the H_2-optimal controller in fact possesses the proposed structure, thereby establishing the optimality of the new controller architecture.
Optimal switching using coherent control
DEFF Research Database (Denmark)
Kristensen, Philip Trøst; Heuck, Mikkel; Mørk, Jesper
2013-01-01
that the switching time, in general, is not limited by the cavity lifetime. Therefore, the total energy required for switching is a more relevant figure of merit than the switching speed, and for a particular two-pulse switching scheme we use calculus of variations to optimize the switching in terms of input energy....
Optimal control, optimization and asymptotic analysis of Purcell's microswimmer model
Wiezel, Oren; Or, Yizhar
2016-11-01
Purcell's swimmer (1977) is a classic model of a three-link microswimmer that moves by performing periodic shape changes. Becker et al. (2003) showed that the swimmer's direction of net motion is reversed upon increasing the stroke amplitude of joint angles. Tam and Hosoi (2007) used numerical optimization in order to find optimal gaits for maximizing either net displacement or Lighthill's energetic efficiency. In our work, we analytically derive leading-order expressions as well as next-order corrections for both net displacement and energetic efficiency of Purcell's microswimmer. Using these expressions enables us to explicitly show the reversal in direction of motion, as well as obtaining an estimate for the optimal stroke amplitude. We also find the optimal swimmer's geometry for maximizing either displacement or energetic efficiency. Additionally, the gait optimization problem is revisited and analytically formulated as an optimal control system with only two state variables, which can be solved using Pontryagin's maximum principle. It can be shown that the optimal solution must follow a "singular arc". Numerical solution of the boundary value problem is obtained, which exactly reproduces Tam and Hosoi's optimal gait.
USING OPTIMAL FEEDBACK CONTROL FOR CHAOS TARGETING
Institute of Scientific and Technical Information of China (English)
PENG ZHAO-WANG; ZHONG TING-XIU
2000-01-01
Since the conventional open-loop optimal targeting of chaos is very sensitive to noise, a close-loop optimal targeting method is proposed to improve the targeting performance under noise. The present optimal targeting model takes into consideration both precision and speed of the targeting procedure. The parameters, rather than the output, of the targeting controller, are directly optimized to obtain optimal chaos targeting. Analysis regarding the mechanism is given from physics aspect and numerical experiment on the Hénon map is carried out to compare the targeting performance under noise between the close-loop and the open-loop methods.
Linear optimal control of tokamak fusion devices
Energy Technology Data Exchange (ETDEWEB)
Kessel, C.E.; Firestone, M.A.; Conn, R.W.
1989-05-01
The control of plasma position, shape and current in a tokamak fusion reactor is examined using linear optimal control. These advanced tokamaks are characterized by non up-down symmetric coils and structure, thick structure surrounding the plasma, eddy currents, shaped plasmas, superconducting coils, vertically unstable plasmas, and hybrid function coils providing ohmic heating, vertical field, radial field, and shaping field. Models of the electromagnetic environment in a tokamak are derived and used to construct control gains that are tested in nonlinear simulations with initial perturbations. The issues of applying linear optimal control to advanced tokamaks are addressed, including complex equilibrium control, choice of cost functional weights, the coil voltage limit, discrete control, and order reduction. Results indicate that the linear optimal control is a feasible technique for controlling advanced tokamaks where the more common classical control will be severely strained or will not work. 28 refs., 13 figs.
Optimal Control of Switched Systems based on Bezier Control Points
Directory of Open Access Journals (Sweden)
FatemeGhomanjani
2012-06-01
Full Text Available This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into k sub-intervals. Second, the trajectory and control functions are approximatedby Bezier curves in each subinterval. Bezier curves have been considered as piecewise polynomials of degree n, then they will be determined by n+1 control points on any subinterval. The optimal control problem is there by converted into a nonlinear programming problem (NLP, which can be solved by known algorithms. However in this paper the MATLAB optimization routine FMINCON is used for solving resulting NLP.
System Optimization by Periodic Control.
1979-09-30
extended re- sults are now contained in a single report [3] which will appear as a regular paper in the December, 1979 issue of the IEEE Transactions on Automatic Control . The...Test Revisited, " to appear in the IEEE Transactions on Automatic Control . 4. D. J. Lyons, "Improved Aircraft Cruise by Periodic Control," Ph. D
A Composite Model Predictive Control Strategy for Furnaces
Institute of Scientific and Technical Information of China (English)
Hao Zang; Hongguang Li; Jingwen Huang; Jia Wang
2014-01-01
Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimi-zation of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The control ers connected with two kinds of communi-cation networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reason-able CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.
Optimal Control Development System for Electrical Drives
Directory of Open Access Journals (Sweden)
Marian GAICEANU
2008-08-01
Full Text Available In this paper the optimal electrical drive development system is presented. It consists of both electrical drive types: DC and AC. In order to implement the optimal control for AC drive system an Altivar 71 inverter, a Frato magnetic particle brake (as load, three-phase induction machine, and dSpace 1104 controller have been used. The on-line solution of the matrix Riccati differential equation (MRDE is computed by dSpace 1104 controller, based on the corresponding feedback signals, generating the optimal speed reference for the AC drive system. The optimal speed reference is tracked by Altivar 71 inverter, conducting to energy reduction in AC drive. The classical control (consisting of rotor field oriented control with PI controllers and the optimal one have been implemented by designing an adequate ControlDesk interface. The three-phase induction machine (IM is controlled at constant flux. Therefore, the linear dynamic mathematical model of the IM has been obtained. The optimal control law provides transient regimes with minimal energy consumption. The obtained solution by integration of the MRDE is orientated towards the numerical implementation-by using a zero order hold. The development system is very useful for researchers, doctoral students or experts training in electrical drive. The experimental results are shown.
Dynamic optimization and adaptive controller design
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
Optimal control of renewable economic resources
Energy Technology Data Exchange (ETDEWEB)
Adelani, L.A.
1987-01-01
Two main problems are studied: economic optimization, and determination of the optimal age of harvest for an initially immature population which follows a Bertalanffy-type growth law. Conditions are derived on the economic parameters that make maximization of economic rent biologically superior to maximization of sustainable yield. A general equation is derived for the optimal equilibrium biomass size when maximization of present value is the control objective. Also, it is shown that under perfectly elastic demand for the resource, a critical price level exists beyond which economic optimization has to be sacrificed in order to enhance conservation of the resource. An equation is derived whose solution represents the optimal age of harvest for an initially immature population stock. In certain circumstances, analytic forms are obtained for the optimal age of harvest. Some properties of the optimal age of harvest are also investigated.
Nonlinear Model Predictive Control for Oil Reservoirs Management
DEFF Research Database (Denmark)
Capolei, Andrea
. With this objective function we link the optimization problem in production optimization to the Markowitz portfolio optimization problem in finance or to the the robust design problem in topology optimization. In this study we focus on open-loop configuration, i.e. without measurement feedback. We demonstrate......, the research community is working on improving current feedback model-based optimal control technologies. The topic of this thesis is production optimization for water flooding in the secondary phase of oil recovery. We developed numerical methods for nonlinear model predictive control (NMPC) of an oil field....... Further, we studied the use of robust control strategies in both open-loop, i.e. without measurement feedback, and closed-loop, i.e. with measurement feedback, configurations. This thesis has three main original contributions: The first contribution in this thesis is to improve the computationally...
Institute of Scientific and Technical Information of China (English)
彭斐; 彭勇刚; 韦巍
2014-01-01
针对由于人工气候箱温湿度的耦合和滞后特性，使其难于精确控制的问题，该文采用模型预测控制算法进行精确控制。基于人工气候箱温湿度控制模型，推导出了输入滞后对象的模型预测控制方法及其优化模型。为了解决模型预测控制的快速优化问题，采用神经动态优化方法作为模型预测控制的动态优化器，获得了基于神经动态优化的模型预测控制方法，并用来解决人工气候箱的温湿度控制问题。最后采用该方法和PID方法针对人工气候箱温湿度的阶跃响应和周期响应进行了仿真试验。试验表明，与常规PID（proportion integration differentiation）控制方法相比，该控制方法超调小，控制精度高，在线优化速度快。该研究可为模型预测控制在时滞系统中的应用提供参考。%Because an artificial climate chest can provide an artificial climate environment, it is widely used in biological, chemical, and agricultural science experiments. The key technology of an artificial climate chest is the accurate control of temperature and humidity. Because of a time delay character and the coupling of temperature and humidity in an artificial climate chest, it is difficult to accurately control the temperature and humidity of an artificial climate chest. Normally, Proportion Integration Differentiation (PID) and a fuzzy control method were used to control the temperature and humidity of an artificial climate chest, but the control accuracy and response speed were not satisfactory. In this paper, model predictive control (MPC) was used to control the temperature and humidity of an artificial climate chest. Online optimization is one of key problems of MPC. The temperature and humidity object of an artificial climate chest is an object with two inputs and two outputs, and the object has the characteristics of time delay and coupling. Based on the model of the temperature and humidity
Optimal Control of Isometric Muscle Dynamics
Directory of Open Access Journals (Sweden)
Robert Rockenfeller
2015-03-01
Full Text Available We use an indirect optimal control approach to calculate the optimal neural stimulation needed to obtain measured isometric muscle forces. The neural stimulation of the nerve system is hereby considered to be a control function (input of the system ’muscle’ that solely determines the muscle force (output. We use a well-established muscle model and experimental data of isometric contractions. The model consists of coupled activation and contraction dynamics described by ordinary differential equations. To validate our results, we perform a comparison with commercial optimal control software.
Optimal Control of Evolution Mixed Variational Inclusions
Energy Technology Data Exchange (ETDEWEB)
Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx [Universidad Nacional Autónoma de México, Departamento de Recursos Naturales, Instituto de Geofísica (Mexico)
2013-12-15
Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.
MDP Optimal Control under Temporal Logic Constraints
Ding, Xu Chu; Belta, Calin; Rus, Daniela
2011-01-01
In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.
Energy Optimal Control of Induction Motor Drives
DEFF Research Database (Denmark)
Abrahamsen, Flemming
This thesis deals with energy optimal control of small and medium-size variable speed induction motor drives for especially Heating, Ventilation and Air-Condition (HVAC) applications. Optimized efficiency is achieved by adapting the magnetization level in the motor to the load, and the basic...... purpose is demonstrate how this can be done for low-cost PWM-VSI drives without bringing the robustness of the drive below an acceptable level. Four drives are investigated with respect to energy optimal control: 2.2 kW standard and high-efficiency motor drives, 22 kW and 90 kW standard motor drives....... The method has been to make extensive efficiency measurements within the specified operating area with optimized efficiency and with constant air-gap flux, and to establish reliable converter and motor loss models based on those measurements. The loss models have been used to analyze energy optimal control...
Optimal Speed Control for Cruising
DEFF Research Database (Denmark)
Blanke, M.
1994-01-01
With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability......With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability...
Optimal Speed Control for Cruising
DEFF Research Database (Denmark)
Blanke, M.
1994-01-01
With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability......With small profit margins in merchant shipping and more than eighty percent of sailing time being cross ocean voyages, speed control is crucial for vessel profitability...
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
New Robust Nonlinear Controller Design Based on Predictive Control for Industrial Processes
Directory of Open Access Journals (Sweden)
Hossein Esfroghy
2007-12-01
Full Text Available In this paper a new sliding mode controller based on predictive control is used for the first order system, which is a good model for the industrial process. In this method a developed predictive control is used to optimize the sliding mode control including sliding surface and switching function coefficient at every moment. A new smooth function is used to reduce the chattering problems. Simulation results show the high effectiveness of the proposed controller.
Greenhouse climate management : an optimal control approach
Henten, van E.J.
1994-01-01
In this thesis a methodology is developed for the construction and analysis of an optimal greenhouse climate control system.
In chapter 1, the results of a literature survey are presented and the research objectives are defined. In the literature, optimal greenhouse climate
Optimal control and the calculus of variations
Pinch, Enid R
1993-01-01
This introduction to optimal control theory is intended for undergraduate mathematicians and for engineers and scientists with some knowledge of classical analysis. It includes sections on classical optimization and the calculus of variations. All the important theorems are carefully proved. There are many worked examples and exercises for the reader to attempt.
Optimization and control of metal forming processes
Havinga, Gosse Tjipke
2016-01-01
Inevitable variations in process and material properties limit the accuracy of metal forming processes. Robust optimization methods or control systems can be used to improve the production accuracy. Robust optimization methods are used to design production processes with low sensitivity to the distu
Greenhouse climate management: an optimal control approach.
Henten, van E.J.
1994-01-01
In this thesis a methodology is developed for the construction and analysis of an optimal greenhouse climate control system.In chapter 1, the results of a literature survey are presented and the research objectives are defined. In the literature, optimal greenhouse climate management systems have be
Fuzzy predictive filtering in nonlinear economic model predictive control for demand response
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.;
2016-01-01
The performance of a model predictive controller (MPC) is highly correlated with the model's accuracy. This paper introduces an economic model predictive control (EMPC) scheme based on a nonlinear model, which uses a branch-and-bound tree search for solving the inherent non-convex optimization...... problem. Moreover, to reduce the computation time and improve the controller's performance, a fuzzy predictive filter is introduced. With the purpose of testing the developed EMPC, a simulation controlling the temperature levels of an intelligent office building (PowerFlexHouse), with and without fuzzy...
Optimal control problems with switching points
Seywald, Hans
1991-09-01
An overview is presented of the problems and difficulties that arise in solving optimal control problems with switching points. A brief discussion of existing optimality conditions is given and a numerical approach for solving the multipoint boundary value problems associated with the first-order necessary conditions of optimal control is presented. Two real-life aerospace optimization problems are treated explicitly. These are altitude maximization for a sounding rocket (Goddard Problem) in the presence of a dynamic pressure limit, and range maximization for a supersonic aircraft flying in the vertical, also in the presence of a dynamic pressure limit. In the second problem singular control appears along arcs with active dynamic pressure limit, which in the context of optimal control, represents a first-order state inequality constraint. An extension of the Generalized Legendre-Clebsch Condition to the case of singular control along state/control constrained arcs is presented and is applied to the aircraft range maximization problem stated above. A contribution to the field of Jacobi Necessary Conditions is made by giving a new proof for the non-optimality of conjugate paths in the Accessory Minimum Problem. Because of its simple and explicit character, the new proof may provide the basis for an extension of Jacobi's Necessary Condition to the case of the trajectories with interior point constraints. Finally, the result that touch points cannot occur for first-order state inequality constraints is extended to the case of vector valued control functions.
Optimized chaos control with simple limiters.
Wagner, C; Stoop, R
2001-01-01
We present an elementary derivation of chaos control with simple limiters using the logistic map and the Henon map as examples. This derivation provides conditions for optimal stabilization of unstable periodic orbits of a chaotic attractor.
Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit
DEFF Research Database (Denmark)
Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan
2012-01-01
We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures asym...
The optimal control and its multiple applications
2009-01-01
In this work we refer to motivations, applications, and relations of control theory with other areas of mathematics. We present a brief historical review of optimal control theory, from its roots in the calculus of variations and the classical theory of control to the present time, giving particular emphasis to the Pontryagin maximum principle.
Multiple Objective Optimization and Optimal Control of Fermentation Processes
Directory of Open Access Journals (Sweden)
Mitko Petrov
2008-10-01
Full Text Available A multiple objective optimization is applied for finding an optimum policy of fed-batch processes of whey fermentation and L-lysine production. The multiple objective optimization problems are transformed to a standard problem of optimization with single objective function by a general utility function with weight coefficients for each single utility coefficient criteria. A combined algorithm is applied when solving the maximizing decision problem. The algorithm includes a method for random search of finding an initial point and a method based on the fuzzy sets theory, combined in order to find the best solution of the optimization problem. The application of the combined algorithm eliminates the main disadvantage of the used fuzzy optimization method, namely it decreases the number of discrete values of the control variables. Thus, the algorithm allows problems with larger scale to be solved. After this multiple optimization, the useful product quality rises and the residual substrate concentration at the end of the process decreases. In this way, the process productivity is increased.
Combining clinical variables to optimize prediction of antidepressant treatment outcomes
Iniesta, R.; Malki, K.; Maier, W; Rietschel, M.; Mors, O; Hauser, J; Henigsberg, N.; Dernovsek, M. Z.; Souery, D.; Stahl, D.; Dobson, R.; Aitchison, K. J.; Farmer, A; Lewis, C.M.; McGuffin, P.
2016-01-01
The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remissio...
Nonlinear Economic Model Predictive Control Strategy for Active Smart Buildings
DEFF Research Database (Denmark)
Santos, Rui Mirra; Zong, Yi; Sousa, Joao M. C.
2016-01-01
Nowadays, the development of advanced and innovative intelligent control techniques for energy management in buildings is a key issue within the smart grid topic. A nonlinear economic model predictive control (EMPC) scheme, based on the branch-and-bound tree search used as optimization algorithm...... for solving the nonconvex optimization problem is proposed in this paper. A simulation using the nonlinear model-based controller to control the temperature levels of an intelligent office building (PowerFlexHouse) is addressed. Its performance is compared with a linear model-based controller. The nonlinear...
Optimal impulse control problems and linear programming.
Bauso, D.
2009-01-01
Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, ...
Neuro-optimal control of helicopter UAVs
Nodland, David; Ghosh, Arpita; Zargarzadeh, H.; Jagannathan, S.
2011-05-01
Helicopter UAVs can be extensively used for military missions as well as in civil operations, ranging from multirole combat support and search and rescue, to border surveillance and forest fire monitoring. Helicopter UAVs are underactuated nonlinear mechanical systems with correspondingly challenging controller designs. This paper presents an optimal controller design for the regulation and vertical tracking of an underactuated helicopter using an adaptive critic neural network framework. The online approximator-based controller learns the infinite-horizon continuous-time Hamilton-Jacobi-Bellman (HJB) equation and then calculates the corresponding optimal control input that minimizes the HJB equation forward-in-time. In the proposed technique, optimal regulation and vertical tracking is accomplished by a single neural network (NN) with a second NN necessary for the virtual controller. Both of the NNs are tuned online using novel weight update laws. Simulation results are included to demonstrate the effectiveness of the proposed control design in hovering applications.
The effects of redundant control inputs in optimal control
Institute of Scientific and Technical Information of China (English)
DUAN ZhiSheng; HUANG Lin; YANG Ying
2009-01-01
For a stabillzable system,the extension of the control inputs has no use for stabllizability,but it is important for optimal control.In this paper,a necessary and sufficient condition is presented to strictly decrease the quadratic optimal performance index after control input extensions.A similar result is also provided for H_2 optimal control problem.These results show an essential difference between single-input and multi-input control systems.Several examples are taken to illustrate related problems.
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
Aprasoff, Jonathan; Donchin, Opher
2012-04-01
Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
Hybrid optimization schemes for quantum control
Energy Technology Data Exchange (ETDEWEB)
Goerz, Michael H.; Koch, Christiane P. [Universitaet Kassel, Theoretische Physik, Kassel (Germany); Whaley, K. Birgitta [University of California, Department of Chemistry, Berkeley, CA (United States)
2015-12-15
Optimal control theory is a powerful tool for solving control problems in quantum mechanics, ranging from the control of chemical reactions to the implementation of gates in a quantum computer. Gradient-based optimization methods are able to find high fidelity controls, but require considerable numerical effort and often yield highly complex solutions. We propose here to employ a two-stage optimization scheme to significantly speed up convergence and achieve simpler controls. The control is initially parametrized using only a few free parameters, such that optimization in this pruned search space can be performed with a simplex method. The result, considered now simply as an arbitrary function on a time grid, is the starting point for further optimization with a gradient-based method that can quickly converge to high fidelities. We illustrate the success of this hybrid technique by optimizing a geometric phase gate for two superconducting transmon qubits coupled with a shared transmission line resonator, showing that a combination of Nelder-Mead simplex and Krotov's method yields considerably better results than either one of the two methods alone. (orig.)
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
OPTIMAL OPERATIONAL CONTROL OF INTERCEPTOR SEWER SYSTEM
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In this paper, a mathematical model was built up to solve the problem of optimal operational control by analysing the factors on an interceptor sewer system and a Fortran program was produced for this model. This paper shows that the optimal control states can be determined by working out the optimal flow rates by means of Linear Programming (LP). The result is very sensitive to interception points and the concentration weight coefficients over time. The result further highlights some practical applications for the existing sewer systems or the sewer systems under design.
Investigation on evolutionary optimization of chaos control
Energy Technology Data Exchange (ETDEWEB)
Zelinka, Ivan [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: zelinka@fai.utb.cz; Senkerik, Roman [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: senkerik@fai.utb.cz; Navratil, Eduard [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: enavratil@fai.utb.cz
2009-04-15
This work deals with an investigation on optimization of the feedback control of chaos based on the use of evolutionary algorithms. The main objective is to show that evolutionary algorithms are capable of optimization of chaos control. As models of deterministic chaotic systems, one-dimensional Logistic equation and two-dimensional Henon map were used. The optimizations were realized in several ways, each one for another set of parameters of evolution algorithms or separate cost functions. The evolutionary algorithm SOMA (self-organizing migrating algorithm) was used in four versions. For each version simulations were repeated several times to show and check for robustness of the applied method.
Han, Lanshan; Camlibel, M. Kanat; Pang, Jong-Shi; Heemels, W. P. Maurice H.
2012-01-01
This paper presents a numerical scheme for solving the continuous-time convex linear-quadratic (LQ) optimal control problem with mixed polyhedral state and control constraints. Unifying a discretization of this optimal control problem as often employed in model predictive control and that obtained
Optimal Wentzell Boundary Control of Parabolic Equations
Energy Technology Data Exchange (ETDEWEB)
Luo, Yousong, E-mail: yousong.luo@rmit.edu.au [RMIT University, School of Mathematical and Geospatial Sciences (Australia)
2017-04-15
This paper deals with a class of optimal control problems governed by an initial-boundary value problem of a parabolic equation. The case of semi-linear boundary control is studied where the control is applied to the system via the Wentzell boundary condition. The differentiability of the state variable with respect to the control is established and hence a necessary condition is derived for the optimal solution in the case of both unconstrained and constrained problems. The condition is also sufficient for the unconstrained convex problems. A second order condition is also derived.
Optimal control problem for the extended Fisher–Kolmogorov equation
Indian Academy of Sciences (India)
Ning Duan
2016-02-01
In this paper, the optimal control problem for the extended Fisher–Kolmogorov equation is studied. The optimal control under boundary condition is given, the existence of optimal solution to the equation is proved and the optimality system is established.
Model-based control of fuel cells (2): Optimal efficiency
Energy Technology Data Exchange (ETDEWEB)
Golbert, Joshua; Lewin, Daniel R. [PSE Research Group, Wolfson Department of Chemical Engineering, Technion IIT, Haifa 32000 (Israel)
2007-11-08
A dynamic PEM fuel cell model has been developed, taking into account spatial dependencies of voltage, current, material flows, and temperatures. The voltage, current, and therefore, the efficiency are dependent on the temperature and other variables, which can be optimized on the fly to achieve optimal efficiency. In this paper, we demonstrate that a model predictive controller, relying on a reduced-order approximation of the dynamic PEM fuel cell model can satisfy setpoint changes in the power demand, while at the same time, minimize fuel consumption to maximize the efficiency. The main conclusion of the paper is that by appropriate formulation of the objective function, reliable optimization of the performance of a PEM fuel cell can be performed in which the main tunable parameter is the prediction and control horizons, V and U, respectively. We have demonstrated that increased fuel efficiency can be obtained at the expense of slower responses, by increasing the values of these parameters. (author)
Muscle synergies may improve optimization prediction of knee contact forces during walking.
Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J
2014-02-01
The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 force predictions improved when
OPTIMAL CONTROL FOR ELECTRIC VEHICLE STABILIZATION
Directory of Open Access Journals (Sweden)
MARIAN GAICEANU
2016-01-01
Full Text Available This main objective of the paper is to stabilize an electric vehicle in optimal manner to a step lane change maneuver. To define the mathematical model of the vehicle, the rigid body moving on a plane is taken into account. An optimal lane keeping controller delivers the adequate angles in order to stabilize the vehicle’s trajectory in an optimal way. Two degree of freedom linear bicycle model is adopted as vehicle model, consisting of lateral and yaw motion equations. The proposed control maintains the lateral stability by taking the feedback information from the vehicle transducers. In this way only the lateral vehicle’s dynamics are enough to considerate. Based on the obtained linear mathematical model the quadratic optimal control is designed in order to maintain the lateral stability of the electric vehicle. The numerical simulation results demonstrate the feasibility of the proposed solution.
Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.
Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad
2016-12-01
Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.
Optimal control of a CSTR process
Directory of Open Access Journals (Sweden)
A. Soukkou
2008-12-01
Full Text Available Designing an effective criterion and learning algorithm for find the best structure is a major problem in the control design process. In this paper, the fuzzy optimal control methodology is applied to the design of the feedback loops of an Exothermic Continuous Stirred Tank Reactor system. The objective of design process is to find an optimal structure/gains of the Robust and Optimal Takagi Sugeno Fuzzy Controller (ROFLC. The control signal thus obtained will minimize a performance index, which is a function of the tracking/regulating errors, the quantity of the energy of the control signal applied to the system, and the number of fuzzy rules. The genetic learning is proposed for constructing the ROFLC. The chromosome genes are arranged into two parts, the binary-coded part contains the control genes and the real-coded part contains the genes parameters representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the ROFLC are compared to these found by the traditional PD controller with Genetic Optimization (PD_GO. Simulations demonstrate that the proposed ROFLC and PD_GO has successfully met the design specifications.
ADAPTIVE GENERALIZED PREDICTIVE CONTROL OF SWITCHED SYSTEMS
Institute of Scientific and Technical Information of China (English)
WANG Yi-jing; WANG Long
2005-01-01
The problem of adaptive generalized predictive control which consists of output prediction errors for a class of switched systems is studied. The switching law is determined by the output predictive errors of a finite number of subsystems. For the single subsystem and multiple subsystems cases, it is proved that the given direct algorithm of generalized predictive control guarantees the global convergence of the system. This algorithm overcomes the inherent drawbacks of the slow convergence and large transient errors for the conventional adaptive control.
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Optimal Control of Active Recoil Mechanisms
1977-02-01
pressures in different chambers, rod pull are available and can be plotted. A linear state feedback control system is proposed to adapt this...desirable. A linear state feedback control system with variable gains is proposed in the report. A separate control law is designed for each...optimization algorithm to choose a feasible solution. 27 3.3 Results for M-37 Recoil Mechanism The linear state feedback control system and
Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant
Institute of Scientific and Technical Information of China (English)
CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian
2007-01-01
This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.
Optimal control of radiator systems; Optimal reglering av radiatorsystem
Energy Technology Data Exchange (ETDEWEB)
Wollerstrand, J.; Ljunggren, P.; Johansson, P.O.
2007-07-01
This report presents results from a study aiming to considerably improve the development towards minimizing the primary return temperature from a district heating (DH) substation by optimizing the control algorithm for the space heating system. The investigation of this research field started about 20 years ago in Sweden when low flow operation of space heating systems was introduced. Following a couple of years of partly confused discussions, the method was accepted by many, but was rejected by others. Our thesis is that further improvement of cooling of DH water is possible when advanced, but robust, control algorithms are used for the space heating system. A space heating system is traditionally designed for a specific constant circulation flow combined with a suitable control curve for the space heating supply temperature as a function of the outdoor temperature. Optimal choice of the control curve varies from case to case and is an issue both we and others have dealt with in previous work. A large step was to derive theoretical control curves for optimal control of the space heating system, with an analysis of how temperature and circulation flow varies with heat load. The estimated gain varies strongly depending on the conditions, however, with realistic conditions it can be as much as 5 deg C decreased DH return temperature on yearly average. To be able to work properly under varying physical circumstances, a control algorithm must be able to combine variation of space heating supply temperature and circulation flow as a function of the heat load. By regulating the rotation speed of the circulation pump this can be achieved. Such regulation can be adjusted for each and every building by regulating a few parameters in a regulator. The results from this work are, that important theoretical knowledge has been completed, to show results systematically and to find support from practical experiments. A hands-on description of the method for optimizing DH water
Total dissolved gas prediction and optimization in RiverWare
Energy Technology Data Exchange (ETDEWEB)
Stewart, Kevin M. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Witt, Adam M. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Hadjerioua, Boualem [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
2015-09-01
Management and operation of dams within the Columbia River Basin (CRB) provides the region with irrigation, hydropower production, flood control, navigation, and fish passage. These various system-wide demands can require unique dam operations that may result in both voluntary and involuntary spill, thereby increasing tailrace levels of total dissolved gas (TDG) which can be fatal to fish. Appropriately managing TDG levels within the context of the systematic demands requires a predictive framework robust enough to capture the operationally related effects on TDG levels. Development of the TDG predictive methodology herein attempts to capture the different modes of hydro operation, thereby making it a viable tool to be used in conjunction with a real-time scheduling model such as RiverWare. The end result of the effort will allow hydro operators to minimize system-wide TDG while meeting hydropower operational targets and constraints. The physical parameters such as spill and hydropower flow proportions, accompanied by the characteristics of the dam such as plant head levels and tailrace depths, are used to develop the empirically-based prediction model. In the broader study, two different models are developed a simplified and comprehensive model. The latter model incorporates more specific bubble physics parameters for the prediction of tailrace TDG levels. The former model is presented herein and utilizes an empirically based approach to predict downstream TDG levels based on local saturation depth, spillway and powerhouse flow proportions, and entrainment effects. Representative data collected from each of the hydro projects is used to calibrate and validate model performance and the accuracy of predicted TDG uptake. ORNL, in conjunction with IIHR - Hydroscience & Engineering, The University of Iowa, carried out model adjustments to adequately capture TDG levels with respect to each plant while maintaining a generalized model configuration. Validation results
Optimization problems for switched systems with impulsive control
Institute of Scientific and Technical Information of China (English)
Junhao HU; Huayou WANG; Xinzhi LIU; Bin LIU
2005-01-01
By using Impulsive Maximum Principal and three stage optimization method,this paper discusses optimization problems for linear impulsive switched systems with hybrid controls,which includes continuous control and impulsive control.The linear quadratic optimization problems without constraints such as optimal hybrid control,optimal stability and optimal switching instants are addressed in detail.These results are applicable to optimal control problems in economics,mechanics,and management.
Predictive Control of Speededness in Adaptive Testing
van der Linden, Wim J.
2009-01-01
An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…
Centralized Stochastic Optimal Control of Complex Systems
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL
2015-01-01
In this paper we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain using the long-run expected average cost per unit time criterion, and we show that the control policy yielding the Pareto optimal solution minimizes the average cost criterion online. The effectiveness of the proposed solution is validated through simulation and compared to the solution derived with dynamic programming using the average cost criterion.
Cyclic Control Optimization for a Smart Rotor
DEFF Research Database (Denmark)
Bergami, Leonardo; Henriksen, Lars Christian
2012-01-01
The paper presents a method to determine cyclic control trajectories for a smart rotor undergoing periodic-deterministic load variations. The control trajectories result from a constrained optimization problem, where the cost function to minimize is given by the variation of the blade root flapwise...... bending moment within a rotor revolution. The method is applied to a rotor equipped with trailing edge flaps, and capable of individual blade pitching. Results show that the optimized cyclic control significantly alleviates the load variations from periodic disturbances; the combination of both cyclic...
An example in linear quadratic optimal control
Weiss, George; Zwart, Heiko J.
1998-01-01
We construct a simple example of a quadratic optimal control problem for an infinite-dimensional linear system based on a shift semigroup. This system has an unbounded control operator. The cost is quadratic in the input and the state, and the weighting operators are bounded. Despite its extreme
An example in linear quadratic optimal control
Weiss, George; Zwart, Heiko J.
1998-01-01
We construct a simple example of a quadratic optimal control problem for an infinite-dimensional linear system based on a shift semigroup. This system has an unbounded control operator. The cost is quadratic in the input and the state, and the weighting operators are bounded. Despite its extreme sim
Optimal control of nonsmooth distributed parameter systems
Tiba, Dan
1990-01-01
The book is devoted to the study of distributed control problems governed by various nonsmooth state systems. The main questions investigated include: existence of optimal pairs, first order optimality conditions, state-constrained systems, approximation and discretization, bang-bang and regularity properties for optimal control. In order to give the reader a better overview of the domain, several sections deal with topics that do not enter directly into the announced subject: boundary control, delay differential equations. In a subject still actively developing, the methods can be more important than the results and these include: adapted penalization techniques, the singular control systems approach, the variational inequality method, the Ekeland variational principle. Some prerequisites relating to convex analysis, nonlinear operators and partial differential equations are collected in the first chapter or are supplied appropriately in the text. The monograph is intended for graduate students and for resea...
Implementation and Optimization of A Fast Inter Prediction Algorithm
Directory of Open Access Journals (Sweden)
Xiaoyu Li
2013-07-01
Full Text Available Audio Video coding Standard is the second generation Source Coding-Decoding standards of China, especially for embedded audio/video platform. This paper proposes an efficient and fast inter prediction algorithm, which is one of the key techniques of Audio Video coding Standard.Reducing of the redundancy in source sequence inter prediction, it could improve the picture quality. The optimization schemes include two aspects,which are the algorithm framework, variables and data structure. Optimized results demonstrate that our algorithm has a notable improvement of the clockcycle efficiency. Furthermore, this research also gives a valuable insight of the combination with quantum information.
Neural Network Predictive Control for Vanadium Redox Flow Battery
Directory of Open Access Journals (Sweden)
Hai-Feng Shen
2013-01-01
Full Text Available The vanadium redox flow battery (VRB is a nonlinear system with unknown dynamics and disturbances. The flowrate of the electrolyte is an important control mechanism in the operation of a VRB system. Too low or too high flowrate is unfavorable for the safety and performance of VRB. This paper presents a neural network predictive control scheme to enhance the overall performance of the battery. A radial basis function (RBF network is employed to approximate the dynamics of the VRB system. The genetic algorithm (GA is used to obtain the optimum initial values of the RBF network parameters. The gradient descent algorithm is used to optimize the objective function of the predictive controller. Compared with the constant flowrate, the simulation results show that the flowrate optimized by neural network predictive controller can increase the power delivered by the battery during the discharge and decrease the power consumed during the charge.
Optimal performance of constrained control systems
Harvey, P. Scott, Jr.; Gavin, Henri P.; Scruggs, Jeffrey T.
2012-08-01
This paper presents a method to compute optimal open-loop trajectories for systems subject to state and control inequality constraints in which the cost function is quadratic and the state dynamics are linear. For the case in which inequality constraints are decentralized with respect to the controls, optimal Lagrange multipliers enforcing the inequality constraints may be found at any time through Pontryagin’s minimum principle. In so doing, the set of differential algebraic Euler-Lagrange equations is transformed into a nonlinear two-point boundary-value problem for states and costates whose solution meets the necessary conditions for optimality. The optimal performance of inequality constrained control systems is calculable, allowing for comparison to previous, sub-optimal solutions. The method is applied to the control of damping forces in a vibration isolation system subjected to constraints imposed by the physical implementation of a particular controllable damper. An outcome of this study is the best performance achievable given a particular objective, isolation system, and semi-active damper constraints.
Computational Methods for Design, Control and Optimization
2007-10-01
34scenario" that applies to channel flows ( Poiseuille flows , Couette flow ) and pipe flows . Over the past 75 years many complex "transition theories" have...other areas of flow control, optimization and aerodynamic design. approximate sensitivity calculations and optimization codes. The effort was built on a...for fluid flow problems. The improved robustness and computational efficiency of this approach makes it practical for a wide class of problems. The
Robust stability in predictive control with soft constraints
DEFF Research Database (Denmark)
Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad
2010-01-01
In this paper we take advantage of the primary and dual Youla parameterizations for setting up a soft constrained model predictive control (MPC) scheme for which stability is guaranteed in face of norm-bounded uncertainties. Under special conditions guarantees are also given for hard input...... consisting of the primary and dual Youla parameter. This is included in the MPC optimization as a constraint on the induced gain of the optimization variable. We illustrate the method with a numerical simulation example....
Optimal model-free prediction from multivariate time series.
Runge, Jakob; Donner, Reik V; Kurths, Jürgen
2015-05-01
Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.
Using string invariants for prediction searching for optimal parameters
Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard
2016-02-01
We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.
FEEDBACK CONTROL OPTIMIZATION FOR SEISMICALLY EXCITED BUILDINGS
Institute of Scientific and Technical Information of China (English)
Xueping Li; Zuguang Ying
2007-01-01
A feedback control optimization method of partially observable linear structures via stationary response is proposed and analyzed with linear building structures equipped with control devices and sensors. First, the partially observable control problem of the structure under horizontal ground acceleration excitation is converted into a completely observable control problem. Then the It(o) stochastic differential equations of the system are derived based on the stochastic averaging method for quasi-integrable Hamiltonian systems and the stationary solution to the Fokker-Plank-Kolmogorov (FPK) equation associated with the It(o) equations is obtained.The performance index in terms of the mean system energy and mean square control force is established and the optimal control force is obtained by minimizing the performance index. Finally, the numerical results for a three-story building structure model under El Centro, Hachinohe,Northridge and Kobe earthquake excitations are given to illustrate the application and the effectiveness of the proposed method.
Multimodel methods for optimal control of aeroacoustics.
Energy Technology Data Exchange (ETDEWEB)
Chen, Guoquan (Rice University, Houston, TX); Collis, Samuel Scott
2005-01-01
A new multidomain/multiphysics computational framework for optimal control of aeroacoustic noise has been developed based on a near-field compressible Navier-Stokes solver coupled with a far-field linearized Euler solver both based on a discontinuous Galerkin formulation. In this approach, the coupling of near- and far-field domains is achieved by weakly enforcing continuity of normal fluxes across a coupling surface that encloses all nonlinearities and noise sources. For optimal control, gradient information is obtained by the solution of an appropriate adjoint problem that involves the propagation of adjoint information from the far-field to the near-field. This computational framework has been successfully applied to study optimal boundary-control of blade-vortex interaction, which is a significant noise source for helicopters on approach to landing. In the model-problem presented here, the noise propagated toward the ground is reduced by 12dB.
Optimal control applications in electric power systems
Christensen, G S; Soliman, S A
1987-01-01
Significant advances in the field of optimal control have been made over the past few decades. These advances have been well documented in numerous fine publications, and have motivated a number of innovations in electric power system engineering, but they have not yet been collected in book form. Our purpose in writing this book is to provide a description of some of the applications of optimal control techniques to practical power system problems. The book is designed for advanced undergraduate courses in electric power systems, as well as graduate courses in electrical engineering, applied mathematics, and industrial engineering. It is also intended as a self-study aid for practicing personnel involved in the planning and operation of electric power systems for utilities, manufacturers, and consulting and government regulatory agencies. The book consists of seven chapters. It begins with an introductory chapter that briefly reviews the history of optimal control and its power system applications and also p...
2016 Network Games, Control, and Optimization Conference
Jimenez, Tania; Solan, Eilon
2017-01-01
This contributed volume offers a collection of papers presented at the 2016 Network Games, Control, and Optimization conference (NETGCOOP), held at the University of Avignon in France, November 23-25, 2016. These papers highlight the increasing importance of network control and optimization in many networking application domains, such as mobile and fixed access networks, computer networks, social networks, transportation networks, and, more recently, electricity grids and biological networks. Covering a wide variety of both theoretical and applied topics in the areas listed above, the authors explore several conceptual and algorithmic tools that are needed for efficient and robust control operation, performance optimization, and better understanding the relationships between entities that may be acting cooperatively or selfishly in uncertain and possibly adversarial environments. As such, this volume will be of interest to applied mathematicians, computer scientists, engineers, and researchers in other relate...
Institute of Scientific and Technical Information of China (English)
任世锦; 王高峰; 李新玉; 杨茂云; 徐桂云
2016-01-01
提出了基于最优实验设计与Laplacian正则化的自适应小波神经网络(Wavelet neural network, WNN)的非线性预测控制算法。该方法迭代地从 WNN 隐含节点候选集选取隐含小波神经元,并使用扩展卡尔曼滤波(Extended Kalman filter,EKF)方法调整该节点参数。为了控制 WNN的复杂度,提出采用 Laplacian正则化和最优实验设计选择重要的 WNN 隐含节点,使用最小描述长度(Minimum de-scription length,MDL)准则确定节点数量。使用在线基于 Gustafson-kesscl(GK)的模糊满意聚类算法确定 WNN 初始参数值和权重更新策略,该策略具有直观性和物理意义。最后给出基于 WNN 线性化模型的预测函数控制方法。对工业焦化装置温度控制进行仿真,结果说明了算法的有效性。%A nonlinear predictive control algorithm based on wavelet neural network (WNN)integrating optimal experimental design with manifold regularization is presented for the complex processes.Firstly, the wavelet hidden nodes are recursively selected from candidate node set to be added into WNN and the optimal parameters of selected nodes are obtained through extended Kalman filter (EKF).The optimum experimental design and Laplacian regularization are then integrated to select salient WNN hidden nodes, and minimum description length (MDL)is utilized to determine the number of hidden nodes.Initial WNN parameters and associated weight updating scheme are provided via an online Gustafson-kesscl (GK)based fuzzy satisfactory clustering algorithm with intuitive interpretation and physic meaning.Fi-nally,a predictive functional control law is given by linearizing WNN.The simulation of industrial coking equipment shows the efficiency of the proposed algorithm.
Predictive Approaches to Control of Complex Systems
Karer, Gorazd
2013-01-01
A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequ...
ADEX optimized adaptive controllers and systems from research to industrial practice
Martín-Sánchez, Juan M
2015-01-01
This book is a didactic explanation of the developments of predictive, adaptive predictive and optimized adaptive control, including the latest methodology of adaptive predictive expert (ADEX) control, and their practical applications. It is focused on the stability perspective, used in the introduction of these methodologies, and is divided into six parts, with exercises and real-time simulations provided for the reader as appropriate. ADEX Optimized Adaptive Controllers and Systems begins with the conceptual and intuitive knowledge of the technology and derives the stability conditions to be verified by the driver block and the adaptive mechanism of the optimized adaptive controller to guarantee achievement of desired control performance. The second and third parts are centered on the design of the driver block and adaptive mechanism, which verify these stability conditions. The authors then proceed to detail the stability theory that supports predictive, adaptive predictive and optimized adaptive control m...
Stochastic Optimal Control Models for Online Stores
Bradonjić, Milan
2011-01-01
We present a model for the optimal design of an online auction/store by a seller. The framework we use is a stochastic optimal control problem. In our setting, the seller wishes to maximize her average wealth level, where she can control her price per unit via her reputation level. The corresponding Hamilton-Jacobi-Bellmann equation is analyzed for an introductory case. We then turn to an empirically justified model, and present introductory analysis. In both cases, {\\em pulsing} advertising strategies are recovered for resource allocation. Further numerical and functional analysis will appear shortly.
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Optimal control application to an Ebola model
Institute of Scientific and Technical Information of China (English)
Ebenezer Bonyah; Kingsley Badu; Samuel Kwesi Asiedu-Addo
2016-01-01
Ebola virus is a severe,frequently fatal illness,with a case fatality rate up to 90%.The outbreak of the disease has been acknowledged by World Health Organization as Public Health Emergency of International Concern.The threat of Ebola in West Africa is still a major setback to the socioeconomic development.Optimal control theory is applied to a system of ordinary differential equations which is modeling Ebola infection through three different routes including contact between humans and a dead body.In an attempt to reduce infection in susceptible population,a preventive control is put in the form of education and campaign and two treatment controls are applied to infected and late-stage infected(super) human population.The Pontryagins maximum principle is employed to characterize optimality control,which is then solved numerically.It is observed that time optimal control is existed in the model.The activation of each control showed a positive reduction of infection.The overall effect of activation of all the controls simultaneously reduced the effort required for the reduction of the infection quickly.The obtained results present a good framework for planning and designing cost-effective strategies for good interventions in dealing with Ebola disease.It is established that in order to reduce Ebola threat all the three controls must be taken into consideration concurrently.
OPTIMAL CONTROL ALGORITHMS FOR SECOND ORDER SYSTEMS
Directory of Open Access Journals (Sweden)
Danilo Pelusi
2013-01-01
Full Text Available Proportional Integral Derivative (PID controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.
Chaos Time Series Prediction Based on Membrane Optimization Algorithms
Directory of Open Access Journals (Sweden)
Meng Li
2015-01-01
Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.
Prediction of RNA Secondary Structure Based on Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
LIU Yuan-ning; DONG Hao; ZHANG Hao; WANG Gang; LI Zhi; CHEN Hui-ling
2011-01-01
A novel method for the prediction of RNA secondary structure was proposed based on the particle swarm optimization(PSO). PSO is known to be effective in solving many different types of optimization problems and known for being able to approximate the global optimal results in the solution space. We designed an efficient objective function according to the minimum free energy, the number of selected stems and the average length of selected stems. We calculated how many legal stems there were in the sequence, and selected some of them to obtain an optimal result using PSO in the right of the objective function. A method based on the improved particle swarm optimization(IPSO) was proposed to predict RNA secondary structure, which consisted of three stages. The first stage was applied to e ncoding the source sequences, and to exploring all the legal stems. Then, a set of encoded stems were created in order to prepare input data for the second stage. In the second stage, IPSO was responsible for structure selection. At last, the optimal result was obtained from the secondary structures selected via IPSO. Nine sequences from the comparative RNA website were selected for the evaluation of the proposed method. Compared with other six methods, the proposed method decreased the complexity and enhanced the sensitivity and specificity on the basis of the experiment results.
Directory of Open Access Journals (Sweden)
Alireza Khosravi
2012-03-01
Full Text Available This paper deals with the design of optimal backstepping controller, by using the chaotic particle swarm optimization (CPSO algorithm to control of chaos in Lure like chaotic system. The backstepping method consists of parameters which could have positive values. The parameters are usually chosen optional by trial and error method. The controlled system provides different behaviors for different values of the parameters. It is necessary to select proper parameters to obtain a good response, because the improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. The proposed optimal backstepping controller without trial and error determines the parameters of backstepping controller automatically and intelligently by minimizing the Integral of Time multiplied Absolute Error (ITAE and squared controller output. Finally, the efficiency of the proposed optimal backstepping controller (OBSC is illustrated by implementing the method on the Lure like chaotic system.
Prediction uncertainty and optimal experimental design for learning dynamical systems
Letham, Benjamin; Letham, Portia A.; Rudin, Cynthia; Browne, Edward P.
2016-06-01
Dynamical systems are frequently used to model biological systems. When these models are fit to data, it is necessary to ascertain the uncertainty in the model fit. Here, we present prediction deviation, a metric of uncertainty that determines the extent to which observed data have constrained the model's predictions. This is accomplished by solving an optimization problem that searches for a pair of models that each provides a good fit for the observed data, yet has maximally different predictions. We develop a method for estimating a priori the impact that additional experiments would have on the prediction deviation, allowing the experimenter to design a set of experiments that would most reduce uncertainty. We use prediction deviation to assess uncertainty in a model of interferon-alpha inhibition of viral infection, and to select a sequence of experiments that reduces this uncertainty. Finally, we prove a theoretical result which shows that prediction deviation provides bounds on the trajectories of the underlying true model. These results show that prediction deviation is a meaningful metric of uncertainty that can be used for optimal experimental design.
Using Chemicals to Optimize Conformance Control in Fractured Reservoirs
Energy Technology Data Exchange (ETDEWEB)
Seright, Randall S.; Liang, Jenn-Tai; Schrader, Richard; Hagstrom II, John; Liu, Jin; Wavrik, Kathryn
1999-09-27
This report describes work performed during the first year of the project, ''Using Chemicals to Optimize Conformance Control in Fractured Reservoirs.'' This research project has three objectives. The first objective is to develop a capability to predict and optimize the ability of gels to reduce permeability to water more than that to oil or gas. The second objective is to develop procedures for optimizing blocking agent placement in wells where hydraulic fractures cause channeling problems. The third objective is to develop procedures to optimize blocking agent placement in naturally fractured reservoirs. This research project consists of three tasks, each of which addresses one of the above objectives. Our work is directed at both injection wells and production wells and at vertical, horizontal, and highly deviated wells.
Optimal Investment Control of Macroeconomic Systems
Institute of Scientific and Technical Information of China (English)
ZHAO Ke-jie; LIU Chuan-zhe
2006-01-01
Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.
Optimal control of anthracnose using mixed strategies.
Fotsa Mbogne, David Jaures; Thron, Christopher
2015-11-01
In this paper we propose and study a spatial diffusion model for the control of anthracnose disease in a bounded domain. The model is a generalization of the one previously developed in [15]. We use the model to simulate two different types of control strategies against anthracnose disease. Strategies that employ chemical fungicides are modeled using a continuous control function; while strategies that rely on cultivational practices (such as pruning and removal of mummified fruits) are modeled with a control function which is discrete in time (though not in space). For comparative purposes, we perform our analyses for a spatially-averaged model as well as the space-dependent diffusion model. Under weak smoothness conditions on parameters we demonstrate the well-posedness of both models by verifying existence and uniqueness of the solution for the growth inhibition rate for given initial conditions. We also show that the set [0, 1] is positively invariant. We first study control by impulsive strategies, then analyze the simultaneous use of mixed continuous and pulse strategies. In each case we specify a cost functional to be minimized, and we demonstrate the existence of optimal control strategies. In the case of pulse-only strategies, we provide explicit algorithms for finding the optimal control strategies for both the spatially-averaged model and the space-dependent model. We verify the algorithms for both models via simulation, and discuss properties of the optimal solutions. Copyright © 2015 Elsevier Inc. All rights reserved.
Optimal Control Design with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
We introduce the family of limited model information control design methods, which construct controllers by accessing the plant's model in a constrained way, according to a given design graph. We investigate the achievable closed-loop performance of discrete-time linear time-invariant plants under a separable quadratic cost performance measure with structured static state-feedback controllers. We find the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. At last, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.
Predictive control of speededness in adaptive testing
van der Linden, Willem J.
2009-01-01
An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already
The effect of prediction error correlation on optimal sensor placement in structural dynamics
Papadimitriou, Costas; Lombaert, Geert
2012-04-01
The problem of estimating the optimal sensor locations for parameter estimation in structural dynamics is re-visited. The effect of spatially correlated prediction errors on the optimal sensor placement is investigated. The information entropy is used as a performance measure of the sensor configuration. The optimal sensor location is formulated as an optimization problem involving discrete-valued variables, which is solved using computationally efficient sequential sensor placement algorithms. Asymptotic estimates for the information entropy are used to develop useful properties that provide insight into the dependence of the information entropy on the number and location of sensors. A theoretical analysis shows that the spatial correlation length of the prediction errors controls the minimum distance between the sensors and should be taken into account when designing optimal sensor locations with potential sensor distances up to the order of the characteristic length of the dynamic problem considered. Implementation issues for modal identification and structural-related model parameter estimation are addressed. Theoretical and computational developments are illustrated by designing the optimal sensor configurations for a continuous beam model, a discrete chain-like stiffness-mass model and a finite element model of a footbridge in Wetteren (Belgium). Results point out the crucial effect the spatial correlation of the prediction errors have on the design of optimal sensor locations for structural dynamics applications, revealing simultaneously potential inadequacies of spatially uncorrelated prediction errors models.
Optimally Controlled Flexible Fuel Powertrain System
Energy Technology Data Exchange (ETDEWEB)
Hakan Yilmaz; Mark Christie; Anna Stefanopoulou
2010-12-31
The primary objective of this project was to develop a true Flex Fuel Vehicle capable of running on any blend of ethanol from 0 to 85% with reduced penalty in usable vehicle range. A research and development program, targeting 10% improvement in fuel economy using a direct injection (DI) turbocharged spark ignition engine was conducted. In this project a gasoline-optimized high-technology engine was considered and the hardware and configuration modifications were defined for the engine, fueling system, and air path. Combined with a novel engine control strategy, control software, and calibration this resulted in a highly efficient and clean FFV concept. It was also intended to develop robust detection schemes of the ethanol content in the fuel integrated with adaptive control algorithms for optimized turbocharged direct injection engine combustion. The approach relies heavily on software-based adaptation and optimization striving for minimal modifications to the gasoline-optimized engine hardware system. Our ultimate objective was to develop a compact control methodology that takes advantage of any ethanol-based fuel mixture and not compromise the engine performance under gasoline operation.
An asymptotically optimal nonparametric adaptive controller
Institute of Scientific and Technical Information of China (English)
郭雷; 谢亮亮
2000-01-01
For discrete-time nonlinear stochastic systems with unknown nonparametric structure, a kernel estimation-based nonparametric adaptive controller is constructed based on truncated certainty equivalence principle. Global stability and asymptotic optimality of the closed-loop systems are established without resorting to any external excitations.
On Optimal Control of a Brownian Motion.
1982-06-01
barriers. Puterman [9] uses diffusion processes to model production and inventory processes. In both cases they assume the existence of a stationary... Puterman , A diffusion model for a storage system, Logistic, M. Geisler ed., North-Holland 197S. [101 J. Rath, The optimal policy for a controlled
Optimizing discrete control systems with phase limitations
Energy Technology Data Exchange (ETDEWEB)
Shakhverdian, S.B.; Abramian, A.K.
1981-01-01
A new method is proposed for solving discrete problems of optimizing control systems with limitations on the phase coordinates. Results are given from experimental research which demonstrate the need to introduce tangential limitations independent of the method of accounting for the phase limitations.
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use
Determination of optimal gains for constrained controllers
Energy Technology Data Exchange (ETDEWEB)
Kwan, C.M.; Mestha, L.K.
1993-08-01
In this report, we consider the determination of optimal gains, with respect to a certain performance index, for state feedback controllers where some elements in the gain matrix are constrained to be zero. Two iterative schemes for systematically finding the constrained gain matrix are presented. An example is included to demonstrate the procedures.
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
Optimization-based controller design for rotorcraft
Tsing, N.-K.; Fan, M. K. H.; Barlow, J.; Tits, A. L.; Tischler, M. B.
1993-01-01
An optimization-based methodology for linear control system design is outlined by considering the design of a controller for a UH-60 rotorcraft in hover. A wide range of design specifications is taken into account: internal stability, decoupling between longitudinal and lateral motions, handling qualities, and rejection of windgusts. These specifications are investigated while taking into account physical limitations in the swashplate displacements and rates of displacement. The methodology crucially relies on user-machine interaction for tradeoff exploration.
Dimension variation prediction and control for composites
Dong, Chensong
This dissertation presents a systematic study on the dimension variation prediction and control for polymer matrix fiber reinforced composites. A dimension variation model was developed for process simulation based on thermal stress analysis and finite element analysis (FEA). This model was validated against the experimental data, the analytical solutions and the data from literature. Using the FEA-based dimension variation model, the deformations of typical composite structures were studied and the regression-based dimension variation model was developed. The regression-based dimension variation model can significantly reduce computation time and provide a quick design guide for composite products with reduced dimension variations. By introducing the material modification coefficient, this comprehensive model can handle various fiber/resin types and stacking sequences. It eliminates the complicated, time-consuming finite element meshing and material parameter defining process. The deformation compensation through tooling design was investigated using the FEA-based and the regression-based dimension variation models. The structural tree method (STM) was developed to compute the assembly deformation from the deformations of individual components, as well as the deformation of general shape composite components. The STM enables rapid dimension variation analysis/synthesis for complex composite assemblies with the regression-based dimension variation model. Using the STM and the regression-based dimension variation model, design optimization and tolerance analysis/synthesis were conducted. The exploring work presented in this research provides a foundation to develop practical and proactive dimension control techniques for composite products.
Combining clinical variables to optimize prediction of antidepressant treatment outcomes.
Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf
2016-07-01
The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.
Predictive Control Based upon State Space Models
Directory of Open Access Journals (Sweden)
Jens G. Balchen
1989-04-01
Full Text Available Repetitive online computation of the control vector by solving the optimal control problem of a non-linear multivariable process with arbitrary performance indices is investigated. Two different methods are considered in the search for an optimal, parameterized control vector: Pontryagin's Maximum Principle and optimization by using the performance index and its gradient directly. Unfortunately, solving this optimization problem has turned out to be a rather time-consuming task which has resulted in a time delay that cannot be accepted when the actual process is exposed to rapidly-varying disturbances. However, an instantaneous feedback strategy operating in parallel with the original control aogorithm was found to be able to cope with this problem.
REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN
Directory of Open Access Journals (Sweden)
A. I. Hinojosa
Full Text Available Abstract In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC, based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.
Interpolation techniques in robust constrained model predictive control
Kheawhom, Soorathep; Bumroongsri, Pornchai
2017-05-01
This work investigates interpolation techniques that can be employed on off-line robust constrained model predictive control for a discrete time-varying system. A sequence of feedback gains is determined by solving off-line a series of optimal control optimization problems. A sequence of nested corresponding robustly positive invariant set, which is either ellipsoidal or polyhedral set, is then constructed. At each sampling time, the smallest invariant set containing the current state is determined. If the current invariant set is the innermost set, the pre-computed gain associated with the innermost set is applied. If otherwise, a feedback gain is variable and determined by a linear interpolation of the pre-computed gains. The proposed algorithms are illustrated with case studies of a two-tank system. The simulation results showed that the proposed interpolation techniques significantly improve control performance of off-line robust model predictive control without much sacrificing on-line computational performance.
Helicopter trajectory planning using optimal control theory
Menon, P. K. A.; Cheng, V. H. L.; Kim, E.
1988-01-01
A methodology for optimal trajectory planning, useful in the nap-of-the-earth guidance of helicopters, is presented. This approach uses an adjoint-control transformation along with a one-dimensional search scheme for generating the optimal trajectories. In addition to being useful for helicopter nap-of-the-earth guidance, the trajectory planning solution is of interest in several other contexts, such as robotic vehicle guidance and terrain-following guidance for cruise missiles and aircraft. A distinguishing feature of the present research is that the terrain constraint and the threat envelopes are incorporated in the equations of motion. Second-order necessary conditions are examined.
Optimization for efficient structure-control systems
Oz, Hayrani; Khot, Narendra S.
1993-01-01
The efficiency of a structure-control system is a nondimensional parameter which indicates the fraction of the total control power expended usefully in controlling a finite-dimensional system. The balance of control power is wasted on the truncated dynamics serving no useful purpose towards the control objectives. Recently, it has been demonstrated that the concept of efficiency can be used to address a number of control issues encountered in the control of dynamic systems such as the spillover effects, selection of a good input configuration and obtaining reduced order control models. Reference (1) introduced the concept and presented analyses of several Linear Quadratic Regulator designs on the basis of their efficiencies. Encouraged by the results of Ref. (1), Ref. (2) introduces an efficiency modal analysis of a structure-control system which gives an internal characterization of the controller design and establishes the link between the control design and the initial disturbances to affect efficient structure-control system designs. The efficiency modal analysis leads to identification of principal controller directions (or controller modes) distinct from the structural natural modes. Thus ultimately, many issues of the structure-control system revolve around the idea of insuring compatibility of the structural modes and the controller modes with each other, the better the match the higher the efficiency. A key feature in controlling a reduced order model of a high dimensional (or infinity-dimensional distributed parameter system) structural dynamic system must be to achieve high efficiency of the control system while satisfying the control objectives and/or constraints. Formally, this can be achieved by designing the control system and structural parameters simultaneously within an optimization framework. The subject of this paper is to present such a design procedure.
Optimal control of vibrational transitions of HCl
Indian Academy of Sciences (India)
KRISHNA REDDY NANDIPATI; ARUN KUMAR KANAKATI
2016-10-01
Control of fundamental and overtone transitions of a vibration are studied for the diatomic molecule, HCl. Specifically, the results of the effect of variation of the penalty factor on the physical attributes of the system (i.e., probabilities) and pulse (i.e., amplitudes) considering three different pulse durations for each value of the penalty factor are shown and discussed. We have employed the optimal control theory to obtain infrared pulses for selective vibrational transitions. The optimization of initial guess field with Gaussian envelope, phrased as maximization of cost functional, is done using the conjugate gradient method. The interaction of the field with the molecule is treated within the semiclassical dipole approximation. The potential and the dipole moment functions used in the calculations of control dynamics are obtained from high level ab-initio calculations.
Recent developments in cooperative control and optimization
Murphey, Robert; Pardalos, Panos
2004-01-01
Over the past several years, cooperative control and optimization has un questionably been established as one of the most important areas of research in the military sciences. Even so, cooperative control and optimization tran scends the military in its scope -having become quite relevant to a broad class of systems with many exciting, commercial, applications. One reason for all the excitement is that research has been so incredibly diverse -spanning many scientific and engineering disciplines. This latest volume in the Cooperative Systems book series clearly illustrates this trend towards diversity and creative thought. And no wonder, cooperative systems are among the hardest systems control science has endeavored to study, hence creative approaches to model ing, analysis, and synthesis are a must! The definition of cooperation itself is a slippery issue. As you will see in this and previous volumes, cooperation has been cast into many different roles and therefore has assumed many diverse meanings. P...
Maximum process problems in optimal control theory
Directory of Open Access Journals (Sweden)
Goran Peskir
2005-01-01
Full Text Available Given a standard Brownian motion (Btt≥0 and the equation of motion dXt=vtdt+2dBt, we set St=max0≤s≤tXs and consider the optimal control problem supvE(Sτ−Cτ, where c>0 and the supremum is taken over all admissible controls v satisfying vt∈[μ0,μ1] for all t up to τ=inf{t>0|Xt∉(ℓ0,ℓ1} with μ0g∗(St, where s↦g∗(s is a switching curve that is determined explicitly (as the unique solution to a nonlinear differential equation. The solution found demonstrates that the problem formulations based on a maximum functional can be successfully included in optimal control theory (calculus of variations in addition to the classic problem formulations due to Lagrange, Mayer, and Bolza.
On the Optimal Controller for LTV Measurement Feedback Control Problem
Institute of Scientific and Technical Information of China (English)
Ting GONG; Yu Feng LU
2011-01-01
In this paper, we consider the measurement feedback control problem for discrete linear time-varying systems within the framework of nest algebra consisting of causal and bounded linear operators. Based on the inner-outer factorization of operators, we reduce the control problem to a distance from a certain operator to a special subspace of a nest algebra and show the existence of the optimal LTV controller in two different ways: one via the characteristic of the subspace in question directly, the other via the duality theory. The latter also gives a new formula for computing the optimal cost.
Optimization Algorithms for Nuclear Reactor Power Control
Energy Technology Data Exchange (ETDEWEB)
Kim, Yeong Min; Oh, Won Jong; Oh, Seung Jin; Chun, Won Gee; Lee, Yoon Joon [Jeju National University, Jeju (Korea, Republic of)
2010-10-15
One of the control techniques that could replace the present conventional PID controllers in nuclear plants is the linear quadratic regulator (LQR) method. The most attractive feature of the LQR method is that it can provide the systematic environments for the control design. However, the LQR approach heavily depends on the selection of cost function and the determination of the suitable weighting matrices of cost function is not an easy task, particularly when the system order is high. The purpose of this paper is to develop an efficient and reliable algorithm that could optimize the weighting matrices of the LQR system
Robust Structured Control Design via LMI Optimization
DEFF Research Database (Denmark)
Adegas, Fabiano Daher; Stoustrup, Jakob
2011-01-01
This paper presents a new procedure for discrete-time robust structured control design. Parameter-dependent nonconvex conditions for stabilizable and induced L2-norm performance controllers are solved by an iterative linear matrix inequalities (LMI) optimization. A wide class of controller...... structures including decentralized of any order, ﬁxed-order dynamic output feedback, static output feedback can be designed robust to polytopic uncertainties. Stability is proven by a parameter-dependent Lyapunov function. Numerical examples on robust stability margins shows that the proposed procedure can...
Optimal coordinated voltage control of power systems
Institute of Scientific and Technical Information of China (English)
LI Yan-jun; HILL David J.; WU Tie-jun
2006-01-01
An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the parameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattern-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.
MULTI-OBJECTIVE PREDICTIVE CONTROL: A SOLUTION USING METAHEURISTICS
Directory of Open Access Journals (Sweden)
Halim Merabti
2014-12-01
Full Text Available The application of multi objective model predictive control approaches is significantly limited with computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics that have been successfully used in solving difficult optimization problems in a reasonable computation time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi variable system. The computation times and the quality of the solution in terms of the smoothness of the control signals and precision of tracking show that MOPSO can be an alternative for real time applications.
A SIMPLIFIED PREDICTIVE CONTROL FOR A SHELL AND TUBE HEAT EXCHANGER
S.RAJASEKARAN,; Dr.T.KANNADASAN
2010-01-01
In this paper a simplified predictive control design is applied for the controlling a temperature of a fluid stream using the shell and tube heat exchanger. The predictive control design based on Dynamic Matrix Control (DMC) involves the complicated inversion computation for higher dimensional matrix. Using DMC for controlling a temperature of the shell and tube heat exchanger, there is still a need for optimization of conversation of energy. The simplified predictive control is based on DMC,...
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LIDong-Mei; WANGZheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.
Optimized Algorithms for Prediction within Robotic Tele-Operative Interfaces
Martin, Rodney A.; Wheeler, Kevin R.; SunSpiral, Vytas; Allan, Mark B.
2006-01-01
Robonaut, the humanoid robot developed at the Dexterous Robotics Laboratory at NASA Johnson Space Center serves as a testbed for human-robot collaboration research and development efforts. One of the primary efforts investigates how adjustable autonomy can provide for a safe and more effective completion of manipulation-based tasks. A predictive algorithm developed in previous work was deployed as part of a software interface that can be used for long-distance tele-operation. In this paper we provide the details of this algorithm, how to improve upon the methods via optimization, and also present viable alternatives to the original algorithmic approach. We show that all of the algorithms presented can be optimized to meet the specifications of the metrics shown as being useful for measuring the performance of the predictive methods. Judicious feature selection also plays a significant role in the conclusions drawn.
A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle
Directory of Open Access Journals (Sweden)
Hongqiang Guo
2013-12-01
Full Text Available Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO. To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Modelling Driver Assitance Systems by Optimal Control
Wang, M.; Daamen, W.; Hoogendoorn, S.P.; Van Arem, B.
2012-01-01
Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper put forward a receding horizon control framework to model driver assistance systems. The accelerations of automated vehicles are determined to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller d...
Samareh, Hossein; Khoshrou, Seyed Hassan; Shahriar, Kourosh; Ebadzadeh, Mohammad Mehdi; Eslami, Mohammad
2017-09-01
When particle's wave velocity resulting from mining blasts exceeds a certain level, then the intensity of produced vibrations incur damages to the structures around the blasting regions. Development of mathematical models for predicting the peak particle velocity (PPV) based on the properties of the wave emission environment is an appropriate method for better designing of blasting parameters, since the probability of incurred damages can considerably be mitigated by controlling the intensity of vibrations at the building sites. In this research, first out of 11 blasting and geo-mechanical parameters of rock masses, four parameters which had the greatest influence on the vibrational wave velocities were specified using regression analysis. Thereafter, some models were developed for predicting the PPV by nonlinear regression analysis (NLRA) and artificial neural network (ANN) with correlation coefficients of 0.854 and 0.662, respectively. Afterward, the coefficients associated with the parameters in the NLRA model were optimized using optimization particle swarm-genetic algorithm. The values of PPV were estimated for 18 testing dataset in order to evaluate the accuracy of the prediction and performance of the developed models. By calculating statistical indices for the test recorded maps, it was found that the optimized model can predict the PPV with a lower error than the other two models. Furthermore, considering the correlation coefficient (0.75) between the values of the PPV measured and predicted by the optimized nonlinear model, it was found that this model possesses a more desirable performance for predicting the PPV than the other two models.
Optimal foraging, not biogenetic law, predicts spider orb web allometry
Gregorič, Matjaž; Kiesbüy, Heine C.; Quiñones Lebrón, Shakira G.; Rozman, Alenka; Agnarsson, Ingi; Kuntner, Matjaž
2013-03-01
The biogenetic law posits that the ontogeny of an organism recapitulates the pattern of evolutionary changes. Morphological evidence has offered some support for, but also considerable evidence against, the hypothesis. However, biogenetic law in behavior remains underexplored. As physical manifestation of behavior, spider webs offer an interesting model for the study of ontogenetic behavioral changes. In orb-weaving spiders, web symmetry often gets distorted through ontogeny, and these changes have been interpreted to reflect the biogenetic law. Here, we test the biogenetic law hypothesis against the alternative, the optimal foraging hypothesis, by studying the allometry in Leucauge venusta orb webs. These webs range in inclination from vertical through tilted to horizontal; biogenetic law predicts that allometry relates to ontogenetic stage, whereas optimal foraging predicts that allometry relates to gravity. Specifically, pronounced asymmetry should only be seen in vertical webs under optimal foraging theory. We show that, through ontogeny, vertical webs in L. venusta become more asymmetrical in contrast to tilted and horizontal webs. Biogenetic law thus cannot explain L. venusta web allometry, but our results instead support optimization of foraging area in response to spider size.
Linear systems optimal and robust control
Sinha, Alok
2007-01-01
Introduction Overview Contents of the Book State Space Description of a Linear System Transfer Function of a Single Input/Single Output (SISO) System State Space Realizations of a SISO System SISO Transfer Function from a State Space Realization Solution of State Space Equations Observability and Controllability of a SISO System Some Important Similarity Transformations Simultaneous Controllability and Observability Multiinput/Multioutput (MIMO) Systems State Space Realizations of a Transfer Function Matrix Controllability and Observability of a MIMO System Matrix-Fraction Description (MFD) MFD of a Transfer Function Matrix for the Minimal Order of a State Space Realization Controller Form Realization from a Right MFD Poles and Zeros of a MIMO Transfer Function Matrix Stability Analysis State Feedback Control and Optimization State Variable Feedback for a Single Input System Computation of State Feedback Gain Matrix for a Multiinput System State Feedback Gain Matrix for a Multi...
Iterative learning control an optimization paradigm
Owens, David H
2016-01-01
This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...
Dynamics of Dengue epidemics using optimal control
Rodrigues, Helena Sofia; Torres, Delfim F M
2010-01-01
We present an application of optimal control theory to Dengue epidemics. This epidemiologic disease is an important theme in tropical countries due to the growing number of infected individuals. The dynamic model is described by a set of nonlinear ordinary differential equations, that depend on the dynamic of the Dengue mosquito, the number of infected individuals, and the people's motivation to combat the mosquito. The cost functional depends not only on the costs of medical treatment of the infected people but also on the costs related to educational and sanitary campaigns. Two approaches to solve the problem are considered: one using optimal control theory, another one by discretizing first the problem and then solving it with nonlinear programming. The results obtained with OC-ODE and IPOPT solvers are given and discussed. We observe that with current computational tools it is easy to obtain, in an efficient way, better solutions to Dengue problems, leading to a decrease of infected mosquitoes and individ...
Mesh refinement strategy for optimal control problems
Paiva, Luis Tiago; Fontes, Fernando,
2013-01-01
International audience; Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform node...
Parameter variations in prediction skill optimization at ECMWF
Ollinaho, P.; Bechtold, P.; Leutbecher, M.; Laine, M.; Solonen, A.; Haario, H.; Järvinen, H.
2013-11-01
Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our target criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the target criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the target criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general target criterion should be developed
Hierarchical Model Predictive Control for Sustainable Building Automation
Directory of Open Access Journals (Sweden)
Barbara Mayer
2017-02-01
Full Text Available A hierarchicalmodel predictive controller (HMPC is proposed for flexible and sustainable building automation. The implications of a building automation system for sustainability are defined, and model predictive control is introduced as an ideal tool to cover all requirements. The HMPC is presented as a development suitable for the optimization of modern buildings, as well as retrofitting. The performance and flexibility of the HMPC is demonstrated by simulation studies of a modern office building, and the perfect interaction with future smart grids is shown.
Optimal control of complex atomic quantum systems
van Frank, S.; Bonneau, M.; Schmiedmayer, J.; Hild, S.; Gross, C.; Cheneau, M.; Bloch, I.; Pichler, T.; Negretti, A.; Calarco, T.; Montangero, S.
2016-10-01
Quantum technologies will ultimately require manipulating many-body quantum systems with high precision. Cold atom experiments represent a stepping stone in that direction: a high degree of control has been achieved on systems of increasing complexity. However, this control is still sub-optimal. In many scenarios, achieving a fast transformation is crucial to fight against decoherence and imperfection effects. Optimal control theory is believed to be the ideal candidate to bridge the gap between early stage proof-of-principle demonstrations and experimental protocols suitable for practical applications. Indeed, it can engineer protocols at the quantum speed limit – the fastest achievable timescale of the transformation. Here, we demonstrate such potential by computing theoretically and verifying experimentally the optimal transformations in two very different interacting systems: the coherent manipulation of motional states of an atomic Bose-Einstein condensate and the crossing of a quantum phase transition in small systems of cold atoms in optical lattices. We also show that such processes are robust with respect to perturbations, including temperature and atom number fluctuations.
Active control of transient rotordynamic vibration by optimal control methods
Palazzolo, A. B.; Lin, R. R.; Alexander, R. M.; Kascak, A. F.
1988-01-01
Although considerable effort has been put into the study of steady state vibration control, there are few methods applicable to transient vibration control of rotorbearing systems. In this paper optimal control theory has been adopted to minimize rotor vibration due to sudden imbalance, e.g., blade loss. The system gain matrix is obtained by choosing the weighting matrices and solving the Riccati equation. Control forces are applied to the system via a feedback loop. A seven mass rotor system is simulated for illustration. A relationship between the number of sensors and the number of modes used in the optimal control model is investigated. Comparisons of responses are made for various configurations of modes, sensors, and actuators. Furthermore, spillover effect is examined by comparing results from collocated and noncollocated sensor configurations. Results show that shaft vibration is significantly attenuated in the closed loop system.
An optimal promotion cost control model for a markovian manpower ...
African Journals Online (AJOL)
An optimal promotion cost control model for a markovian manpower system. ... Log in or Register to get access to full text downloads. ... A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian ...
Neuro-fuzzy predictive control for nonlinear application
Institute of Scientific and Technical Information of China (English)
CHEN Dong-xiang; WANG Gang; LV Shi-xia
2008-01-01
Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.
Automatic Synthesis of Robust and Optimal Controllers
DEFF Research Database (Denmark)
Cassez, Franck; Jessen, Jan Jacob; Larsen, Kim Guldstrand;
2009-01-01
In this paper, we show how to apply recent tools for the automatic synthesis of robust and near-optimal controllers for a real industrial case study. We show how to use three different classes of models and their supporting existing tools, Uppaal-TiGA for synthesis, phaver for verification......, and Simulink for simulation, in a complementary way. We believe that this case study shows that our tools have reached a level of maturity that allows us to tackle interesting and relevant industrial control problems....
Tractable problems in optimal decentralized control
Rotkowitz, Michael Charles
2005-07-01
This thesis considers the problem of constructing optimal decentralized controllers. The problem is formulated as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. The notion of quadratic invariance of a constraint set with respect to a system is defined. It is shown that quadratic invariance is necessary and sufficient for the constraint set to be preserved under feedback. It is further shown that if the constraint set has this property, this allows the constrained minimum-norm problem to be solved via convex programming. These results are developed in a very general framework, and are shown to hold for continuous-time systems, discrete-time systems, or operators on Banach spaces, for stable or unstable plants, and for the minimization of any norm. The utility of these results is then demonstrated on some specific constraint classes. An explicit test is derived for sparsity constraints on a controller to be quadratically invariant, and thus amenable to convex synthesis. Symmetric synthesis is also shown to be quadratically invariant. The problem of control over networks with delays is then addressed as another constraint class. Multiple subsystems are considered, each with its own controller, such that the dynamics of each subsystem may affect those of other subsystems with some propagation delays, and the controllers may communicate with each other with some transmission delays. It is shown that if the communication delays are less than the propagation delays, then the associated constraints are quadratically invariant, and thus optimal controllers can be synthesized. We further show that this result still holds in the presence of computational delays. This thesis unifies the few previous results on specific tractable decentralized control problems, identifies broad and useful classes of new solvable problems, and delineates the largest known class of convex problems in decentralized control.
Protein structure prediction using bee colony optimization metaheuristic
DEFF Research Database (Denmark)
Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel
2010-01-01
Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation of the pr......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested...... in the literature. We have devised a new variant that uni¿es the existing and is much more ¿exible with respect to replacing the various elements of the BCO. In particular this applies to the choice of the local search as well as the method for generating scout locations and performing the waggle dance. We apply...
Neural Network Predictive Control Based Power System Stabilizer
Directory of Open Access Journals (Sweden)
Ali Mohamed Yousef
2012-04-01
Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.
On necessary optimality conditions in discrete control systems
Mardanov, M. J.; Melikov, T. K.; Mahmudov, N. I.
2015-10-01
The paper deals with a nonlinear discrete-time optimal control problem with a cost functional of terminal type. Using a new variation of the control and new properties of optimal controls, we prove the linearised optimality conditions extending such classical optimality conditions. Along with this, various optimality conditions of quasi-singular controls are obtained. Finally, the examples illustrating the rich content of the obtained results are illustrated.
Catalytic cracking models developed for predictive control purposes
Directory of Open Access Journals (Sweden)
Dag Ljungqvist
1993-04-01
Full Text Available The paper deals with state-space modeling issues in the context of model-predictive control, with application to catalytic cracking. Emphasis is placed on model establishment, verification and online adjustment. Both the Fluid Catalytic Cracking (FCC and the Residual Catalytic Cracking (RCC units are discussed. Catalytic cracking units involve complex interactive processes which are difficult to operate and control in an economically optimal way. The strong nonlinearities of the FCC process mean that the control calculation should be based on a nonlinear model with the relevant constraints included. However, the model can be simple compared to the complexity of the catalytic cracking plant. Model validity is ensured by a robust online model adjustment strategy. Model-predictive control schemes based on linear convolution models have been successfully applied to the supervisory dynamic control of catalytic cracking units, and the control can be further improved by the SSPC scheme.
Data-Driven Predictive Direct Load Control of Refrigeration Systems
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Knudsen, Torben; Wisniewski, Rafal
2015-01-01
A predictive control using subspace identification is applied for the smart grid integration of refrigeration systems under a direct load control scheme. A realistic demand response scenario based on regulation of the electrical power consumption is considered. A receding horizon optimal control...... is proposed to fulfil two important objectives: to secure high coefficient of performance and to participate in power consumption management. Moreover, a new method for design of input signals for system identification is put forward. The control method is fully data driven without an explicit use of model...
Nonlinear Predictive Control for PEMFC Stack Operation Temperature
Institute of Scientific and Technical Information of China (English)
LI Xi; CAO Guang-yi; ZHU Xin-jian
2005-01-01
Operating temperature of proton exchange membrane fuel cell stack should be controlled within a special range. The input-output data and operating experiences were used to establish a PEMFC stack model and operating temperature control system. A nonlinear predictive control algorithm based on fuzzy model was presented for a family of complex system with severe nonlinearity such as PEMFC. Based on the obtained fuzzy model, a discrete optimization of the control action was carried out according to the principle of Branch and Bound method. The test results demonstrate the effectiveness and advantage of this approach.
Applied optimal control theory of distributed systems
Lurie, K A
1993-01-01
This book represents an extended and substantially revised version of my earlierbook, Optimal Control in Problems ofMathematical Physics,originally published in Russian in 1975. About 60% of the text has been completely revised and major additions have been included which have produced a practically new text. My aim was to modernize the presentation but also to preserve the original results, some of which are little known to a Western reader. The idea of composites, which is the core of the modern theory of optimization, was initiated in the early seventies. The reader will find here its implementation in the problem of optimal conductivity distribution in an MHD-generatorchannel flow.Sincethen it has emergedinto an extensive theory which is undergoing a continuous development. The book does not pretend to be a textbook, neither does it offer a systematic presentation of the theory. Rather, it reflects a concept which I consider as fundamental in the modern approach to optimization of dis tributed systems. ...
A Course in... Model Predictive Control.
Arkun, Yaman; And Others
1988-01-01
Describes a graduate engineering course which specializes in model predictive control. Lists course outline and scope. Discusses some specific topics and teaching methods. Suggests final projects for the students. (MVL)
An Iterative Approach for Distributed Model Predictive Control of Irrigation Canals
Doan, D.; Keviczky, T.; Negenborn, R.R.; De Schutter, B.
2009-01-01
Optimization techniques have played a fundamental role in designing automatic control systems for the most part of the past half century. This dependence is ever more obvious in today’s wide-spread use of online optimization-based control methods, such as Model Predictive Control (MPC) [1]. The abil
Predictive Duty Cycle Control of Three-Phase Active-Front-End Rectifiers
DEFF Research Database (Denmark)
Song, Zhanfeng; Tian, Yanjun; Chen, Wei;
2016-01-01
of optimal duty cycles is made by predicting the effect of duty cycles on instantaneous current variations and minimizing the cost function. Due to the adoption of behavior prediction, the proposed controller inherits the excellent dynamic characteristics of predictive controllers. Moreover, the application......This paper proposed an on-line optimizing duty cycle control approach for three-phase active-front-end rectifiers, aiming to obtain the optimal control actions under different operating conditions. Similar to finite control set model predictive control strategy, a cost function previously...... of optimal duty cycles determined by cost function minimization automatically ensures optimum operations of converters within each sampling period. Improved transient and steady-state features of the proposed strategy are confirmed by experimental validations and in-depth comparisons with linear controllers...
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LI Dong-Mei; WANG Zheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.
Optimal control of circular cylinder wakes using long control horizons
Flinois, Thibault L B
2015-01-01
The classical problem of minimizing the drag of a circular cylinder by using body rotation is revisited in an adjoint-based optimal control framework. The cylinder's unsteady and fully unconstrained rotation rate is optimized at Reynolds numbers of 100 and 200 and over horizons that are longer than in previous studies, where they are typically of the order of a vortex shedding period or shorter. In the best configuration, the drag is reduced by $19\\%$, the vortex shedding is effectively suppressed, and this low drag state is maintained with minimal cylinder rotation after transients. Without closed-loop control, which maintains a specific phase relationship between the actuation and the shedding, the wake is not stabilized. A comparison is also given between the performance of optimizations for different horizon lengths and cost functions. It is shown that the long horizons used are necessary in order to stabilize the vortex shedding efficiently.
Price-based Optimal Control of Electrical Power Systems
Energy Technology Data Exchange (ETDEWEB)
Jokic, A.
2007-09-10
The research presented in this thesis is motivated by the following issue of concern for the operation of future power systems: Future power systems will be characterized by significantly increased uncertainties at all time scales and, consequently, their behavior in time will be difficult to predict. In Chapter 2 we will present a novel explicit, dynamic, distributed feedback control scheme that utilizes nodal-prices for real-time optimal power balance and network congestion control. The term explicit means that the controller is not based on solving an optimization problem on-line. Instead, the nodal prices updates are based on simple, explicitly defined and easily comprehensible rules. We prove that the developed control scheme, which acts on the measurements from the current state of the system, always provide the correct nodal prices. In Chapter 3 we will develop a novel, robust, hybrid MPC control (model predictive controller) scheme for power balance control with hard constraints on line power flows and network frequency deviations. The developed MPC controller acts in parallel with the explicit controller from Chapter 2, and its task is to enforce the constraints during the transient periods following suddenly occurring power imbalances in the system. In Chapter 4 the concept of autonomous power networks will be presented as a concise formulation to deal with economic, technical and reliability issues in power systems with a large penetration of distributed generating units. With autonomous power networks as new market entities, we propose a novel operational structure of ancillary service markets. In Chapter 5 we will consider the problem of controlling a general linear time-invariant dynamical system to an economically optimal operating point, which is defined by a multiparametric constrained convex optimization problem related with the steady-state operation of the system. The parameters in the optimization problem are values of the exogenous inputs to
Neural network based optimal control of HVAC&R systems
Ning, Min
Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the
Fast Algorithm of Multivariable Generalized Predictive Control
Institute of Scientific and Technical Information of China (English)
Jin,Yuanyu; Pang,Zhonghua; Cui,Hong
2005-01-01
To avoid the shortcoming of the traditional (previous)generalized predictive control (GPC) algorithms, too large amounts of computation, a fast algorithm of multivariable generalized predictive control is presented in which only the current control actions are computed exactly on line and the rest (the future control actions) are approximately done off line. The algorithm is simple and can be used in the arbitary-dimension input arbitary-dimension output (ADIADO) linear systems. Because it dose not need solving Diophantine equation and reduces the dimension of the inverse matrix, it decreases largely the computational burden. Finally, simulation results show that the presented algorithm is effective and practicable.
Delta-Domain Predictive Control and Identification for Control
DEFF Research Database (Denmark)
Lauritsen, Morten Bach
1997-01-01
The present thesis is concerned with different aspects of modelling, control and identification of linear systems. Traditionally, discrete-time sampled-data systems are represented using shift-operator parametrizations. Such parametrizations are not suitable at fast sampling rates. An alternative...... minimum-variance predictor as a special case and to have a well-defined continuous-time limit. By means of this new prediction method a unified framework for discrete-time and continuous-time predictive control algorithms is developed. This contains a continuous-time like discrete-time predictive...... controller which is insensitive to the choice of sampling period and has a well-defined limit in the continuous-time case. Also more conventional discrete-time predictive control methods may be described within the unified approach. The predictive control algorithms are extended to frequency weighted...
Blasting neuroblastoma using optimal control of chemotherapy.
Collins, Craig; Fister, K Renee; Key, Bethany; Williams, Mary
2009-07-01
A mathematical model is used to investigate the effectiveness of the chemotherapy drug Topotecan against neuroblastoma. Optimal control theory is applied to minimize the tumor volume and the amount of drug utilized. The model incorporates a state constraint that requires the level of circulating neutrophils (white blood cells that form an integral part of the immune system) to remain above an acceptable value. The treatment schedule is designed to simultaneously satisfy this constraint and achieve the best results in fighting the tumor. Existence and uniqueness of the solution of the optimality system, which is the state system coupled with the adjoint system, is established. Numerical simulations are given to demonstrate the behavior of the tumor and the immune system components represented in the model.
Optimization-Based Approaches to Control of Probabilistic Boolean Networks
Directory of Open Access Journals (Sweden)
Koichi Kobayashi
2017-02-01
Full Text Available Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs, which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
Intelligent Predictive Control of Nonlienar Processes Using
DEFF Research Database (Denmark)
Nørgård, Peter Magnus; Sørensen, Paul Haase; Poulsen, Niels Kjølstad
1996-01-01
frequently been discussed in the neural network community. This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting......This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has...... linear models from a nonlinear neural network and using them in designing the control system. The performance of the controller is demonstrated in a simulation study of a pneumatic servo system...
A New Particle Swarm Optimization Based Stock Market Prediction Technique
Directory of Open Access Journals (Sweden)
Essam El. Seidy
2016-04-01
Full Text Available Over the last years, the average person's interest in the stock market has grown dramatically. This demand has doubled with the advancement of technology that has opened in the International stock market, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, the analysis and prediction of future values and trends of the financial markets have got more attention, and due to large applications in different business transactions, stock market prediction has become a critical topic of research. In this paper, our earlier presented particle swarm optimization with center of mass technique (PSOCoM is applied to the task of training an adaptive linear combiner to form a new stock market prediction model. This prediction model is used with some common indicators to maximize the return and minimize the risk for the stock market. The experimental results show that the proposed technique is superior than the other PSO based models according to the prediction accuracy.
Optimal control of HIV/AIDS dynamic: Education and treatment
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
Optimal control of batch emulsion polymerization of vinyl chloride
Energy Technology Data Exchange (ETDEWEB)
Damslora, Andre Johan
1998-12-31
The highly exothermic polymerization of vinyl chloride (VC) is carried out in large vessels where the heat removal represents a major limitation of the production rate. Many emulsion polymerization reactors are operated in such a way that a substantial part of the heat transfer capacity is left unused for a significant part of the total batch time. To increase the reaction rate so that it matches the heat removal capacity during the course of the reaction, this thesis proposes the use of a sufficiently flexible initiator system to obtain a reaction rate which is high throughout the reaction and real-time optimization to compute the addition policy for the initiator. This optimization based approach provides a basis for an interplay between design and control and between production and research. A simple model is developed for predicting the polymerization rate. The model is highly nonlinear and open-loop unstable and may serve as an interesting case for comparison of nonlinear control strategies. The model is fitted to data obtained in a laboratory scale reactor. Finally, the thesis discusses optimal control of the emulsion polymerization reactor. Reduction of the batch cycle time is of major economic importance, as long as the quality parameters are within their specifications. The control parameterization had a major influence on the performance. A differentiable spline parameterization was applied and the optimization is illustrated in a number of cases. The best performance is obtained when the reactor temperature is obtained when the optimization is combined with some form of closed-loop control of the reactor temperature. 112 refs., 48 figs., 4 tabs.
Evolutionary optimization of kernel weights improves protein complex comembership prediction.
Hulsman, Marc; Reinders, Marcel J T; de Ridder, Dick
2009-01-01
In recent years, more and more high-throughput data sources useful for protein complex prediction have become available (e.g., gene sequence, mRNA expression, and interactions). The integration of these different data sources can be challenging. Recently, it has been recognized that kernel-based classifiers are well suited for this task. However, the different kernels (data sources) are often combined using equal weights. Although several methods have been developed to optimize kernel weights, no large-scale example of an improvement in classifier performance has been shown yet. In this work, we employ an evolutionary algorithm to determine weights for a larger set of kernels by optimizing a criterion based on the area under the ROC curve. We show that setting the right kernel weights can indeed improve performance. We compare this to the existing kernel weight optimization methods (i.e., (regularized) optimization of the SVM criterion or aligning the kernel with an ideal kernel) and find that these do not result in a significant performance improvement and can even cause a decrease in performance. Results also show that an expert approach of assigning high weights to features with high individual performance is not necessarily the best strategy.
Model predictive control for a thermostatic controlled system
DEFF Research Database (Denmark)
Shafiei, Seyed Ehsan; Rasmussen, Henrik; Stoustrup, Jakob
2013-01-01
This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff temperat......This paper proposes a model predictive control scheme to provide temperature set-points to thermostatic controlled cooling units in refrigeration systems. The control problem is formulated as a convex programming problem to minimize the overall operating cost of the system. The foodstuff...
A Study of Electrical Motors Controlling Optimization Methods
Directory of Open Access Journals (Sweden)
Saeid Fatemi
2013-11-01
Full Text Available In order to design an efficient motor cooling system, it is important to accurately predict the power optimization which is normally dissipated in form of heat. This study presents an analytical method for estimating bearing frictional optimization and numerical method for estimating electromagnetic optimization for an electric vehicle electrical motor. The power optimization obtained use heat sources when evaluating the thermal performance of the motor. The results showed that electromagnetic optimization are dominant and contributed over 80% of all optimization, while bearing optimization contributes about 2% of the total electric motor. The results also showed that bearing optimization increase significantly with increasing speed or load.
Fractional conservation laws in optimal control theory
Frederico, Gastao S F
2007-01-01
Using the recent formulation of Noether's theorem for the problems of the calculus of variations with fractional derivatives, the Lagrange multiplier technique, and the fractional Euler-Lagrange equations, we prove a Noether-like theorem to the more general context of the fractional optimal control. As a corollary, it follows that in the fractional case the autonomous Hamiltonian does not define anymore a conservation law. Instead, it is proved that the fractional conservation law adds to the Hamiltonian a new term which depends on the fractional-order of differentiation, the generalized momentum, and the fractional derivative of the state variable.
Optimal control of Rydberg lattice gases
Cui, Jian; van Bijnen, Rick; Pohl, Thomas; Montangero, Simone; Calarco, Tommaso
2017-09-01
We present optimal control protocols to prepare different many-body quantum states of Rydberg atoms in optical lattices. Specifically, we show how to prepare highly ordered many-body ground states, GHZ states as well as some superposition of symmetric excitation number Fock states, that inherit the translational symmetry from the Hamiltonian, within sufficiently short excitation times minimising detrimental decoherence effects. For the GHZ states, we propose a two-step detection protocol to experimentally verify the optimised preparation of the target state based only on standard measurement techniques. Realistic experimental constraints and imperfections are taken into account by our optimisation procedure making it applicable to ongoing experiments.
Wing Warping, Roll Control and Aerodynamic Optimization of Inflatable Wings
Simpson, Andrew
2005-11-01
The research presents work on aerodynamic control by warping inflatable wings. Inflatable wings are deformable by their nature. Mechanical manipulation of the wing's shape has been demonstrated to alter the performance and control the vehicle in flight by deforming the trailing edge of the wing near the wing tip. Predicting and correlating the forces required in deforming the wings to a particular shape and the deformation generated for a given internal pressure were conducted through the use of photogrammetry. This research focuses on optimizing the roll moments and aerodynamic performance of the vehicle, given the current level of wing warping ability. Predictions from lifting line theory applied to wing shape changes are presented. Comparisons from the experimental results are made with lifting line analysis for wings with arbitrary twist and the solutions are used to determine rolling moment and optimum L/D. Results from flight tests will also be presented.
Introducing Model Predictive Control for Improving Power Plant Portfolio Performance
DEFF Research Database (Denmark)
Edlund, Kristian Skjoldborg; Bendtsen, Jan Dimon; Børresen, Simon
2008-01-01
This paper introduces a model predictive control (MPC) approach for construction of a controller for balancing the power generation against consumption in a power system. The objective of the controller is to coordinate a portfolio consisting of multiple power plant units in the effort to perform...... reference tracking and disturbance rejection in an economically optimal way. The performance function is chosen as a mixture of the `1-norm and a linear weighting to model the economics of the system. Simulations show a significant improvement of the performance of the MPC compared to the current...
Predictive control applied to an evaporator mathematical model
Directory of Open Access Journals (Sweden)
Daniel Alonso Giraldo Giraldo
2010-07-01
Full Text Available This paper outlines designing a predictive control model (PCM applied to a mathematical model of a falling film evaporator with mechanical steam compression like those used in the dairy industry. The controller was designed using the Connoisseur software package and data gathered from the simulation of a non-linear mathematical model. A control law was obtained from minimising a cost function sublect to dynamic system constraints, using a quadratic programme (QP algorithm. A linear programming (LP algorithm was used for finding a sub-optimal operation point for the process in stationary state.
Economic Model Predictive Control for Spray Drying Plants
DEFF Research Database (Denmark)
Petersen, Lars Norbert
and a complexity reduced control model is used for state estimation and prediction in the controllers. These models facilitate development and comparison of control strategies. We develop two MPC strategies; a linear tracking MPC with a Real-Time Optimization layer (MPC with RTO) and an Economic Nonlinear MPC (E...... and sticky powder is avoided from building up on the dryer walls; 3) Demonstrate the industrial application of an MPC strategy to a full-scale industrial four-stage spray dryer. The main scientific contributions can be summarized to: - Modeling of a four-stage spray dryer. We develop new first...
Model predictive control for wind power gradients
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Boyd, Stephen; Jørgensen, John Bagterp
2015-01-01
ranges. The system dynamics are quite non-linear, and the constraints and objectives are not convex functions of the control inputs, so the resulting optimal control problem is difficult to solve globally. In this paper, we show that by a novel change of variables, which focuses on power flows, we can......We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy generated while respecting limits on the time derivative (gradient) of power delivered to the grid. We...... transform the problem to one with linear dynamics and convex constraints. Thus, the problem can be globally solved, using robust, fast solvers tailored for embedded control applications. We implement the optimal control problem in a receding horizon manner and provide extensive closed-loop tests with real...
Optimal Control of Non-well-posed Heat Equations
Institute of Scientific and Technical Information of China (English)
Geng Sheng WANG
2005-01-01
This work is concerned with Pontryagin's maximum principle of optimal control problems governed by some non-well-posed semilinear heat equations. A type of approach to the non-well-posed optimal control problem is given.
Optimal Control of Pseudoparabolic Variational Inequalities Involving State Constraint
Directory of Open Access Journals (Sweden)
Youjun Xu
2014-01-01
Full Text Available We establish the necessary condition of optimality for optimal control problem governed by some pseudoparabolic differential equations involving monotone graphs. Some approximating control process and examples are given.
Application of Model Predictive Control to BESS for Microgrid Control
Directory of Open Access Journals (Sweden)
Thai-Thanh Nguyen
2015-08-01
Full Text Available Battery energy storage systems (BESSs have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC and MPC based on PI control in the outer and predictive current control (PCC in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.
Optimal Control of Finite Dimensional Quantum Systems
Mendonca, Paulo E M F
2009-01-01
This thesis addresses the problem of developing a quantum counter-part of the well established classical theory of control. We dwell on the fundamental fact that quantum states are generally not perfectly distinguishable, and quantum measurements typically introduce noise in the system being measured. Because of these, it is generally not clear whether the central concept of the classical control theory -- that of observing the system and then applying feedback -- is always useful in the quantum setting. We center our investigations around the problem of transforming the state of a quantum system into a given target state, when the system can be prepared in different ways, and the target state depends on the choice of preparation. We call this the "quantum tracking problem" and show how it can be formulated as an optimization problem that can be approached both numerically and analytically. This problem provides a simple route to the characterization of the quantum trade-off between information gain and distu...
Reproducibility, controllability, and optimization of LENR experiments
Energy Technology Data Exchange (ETDEWEB)
Nagel, David J. [The George Washington University, Washington DC 20052 (United States)
2006-07-01
Low-energy nuclear reaction (LENR) measurements are significantly, and increasingly reproducible. Practical control of the production of energy or materials by LENR has yet to be demonstrated. Minimization of costly inputs and maximization of desired outputs of LENR remain for future developments. The paper concludes by underlying that it is now clearly that demands for reproducible experiments in the early years of LENR experiments were premature. In fact, one can argue that irreproducibility should be expected for early experiments in a complex new field. As emphasized in the paper and as often happened in the history of science, experimental and theoretical progress can take even decades. It is likely to be many years before investments in LENR experiments will yield significant returns, even for successful research programs. However, it is clearly that a fundamental understanding of the anomalous effects observed in numerous experiments will significantly increase reproducibility, improve controllability, enable optimization of processes, and accelerate the economic viability of LENR.
Institute of Scientific and Technical Information of China (English)
Changshui Feng; Weiqiu Zhu
2008-01-01
A bounded optimal control strategy for strongly non-linear systems under non-white wide-band random excitation with actuator saturation is proposed. First, the stochastic averaging method is introduced for controlled strongly non-linear systems under wide-band random excitation using generalized harmonic functions. Then, the dynamical programming equation for the saturated control problem is formulated from the partially averaged Ito equation based on the dynamical programming principle. The optimal control consisting of the unbounded optimal control and the bounded bang-bang control is determined by solving the dynamical programming equation. Finally, the response of the optimally controlled system is predicted by solving the reduced Fokker-Planck-Kolmogorov (FPK) equation associated with the completed averaged Ito equation. An example is given to illustrate the proposed control strategy. Numerical results show that the proposed control strategy has high control effectiveness and efficiency and the chattering is reduced significantly comparing with the bang-bang control strategy.
Optimal second order sliding mode control for nonlinear uncertain systems.
Das, Madhulika; Mahanta, Chitralekha
2014-07-01
In this paper, a chattering free optimal second order sliding mode control (OSOSMC) method is proposed to stabilize nonlinear systems affected by uncertainties. The nonlinear optimal control strategy is based on the control Lyapunov function (CLF). For ensuring robustness of the optimal controller in the presence of parametric uncertainty and external disturbances, a sliding mode control scheme is realized by combining an integral and a terminal sliding surface. The resulting second order sliding mode can effectively reduce chattering in the control input. Simulation results confirm the supremacy of the proposed optimal second order sliding mode control over some existing sliding mode controllers in controlling nonlinear systems affected by uncertainty.
Stability analysis of generalized predictive control based on Kleinman's controllers
Institute of Scientific and Technical Information of China (English)
DING Baocang; XI Yugeng
2004-01-01
With Kleinman's controller, its extended form and Riccati iteration as analyzing tools, the stability of GPC under various parameter cases is discussed. The overall closed-loop stability conclusions of GPC in equivalence with Kleinman's controller are obtained, which cover some existing results and provide the theoretical foundation for stable design of predictive control.
Hierarchical Model Predictive Control for Resource Distribution
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, K; Stoustrup, Jakob
2010-01-01
This paper deals with hierarchichal model predictive control (MPC) of distributed systems. A three level hierachical approach is proposed, consisting of a high level MPC controller, a second level of so-called aggregators, controlled by an online MPC-like algorithm, and a lower level of autonomous...... facilitates plug-and-play addition of subsystems without redesign of any controllers. The method is supported by a number of simulations featuring a three-level smart-grid power control system for a small isolated power grid....
General predictive control using the delta operator
DEFF Research Database (Denmark)
Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad; Ravn, Ole
1993-01-01
This paper deals with two-discrete-time operators, the conventional forward shift-operator and the δ-operator. Both operators are treated in view of construction of suitable solutions to the Diophantine equation for the purpose of prediction. A general step-recursive scheme is presented. Finally...... a general predictive control (GPC) is formulated and applied adaptively to a continuous-time plant...
Optimal control of quantum systems by chirped pulses
DEFF Research Database (Denmark)
Amstrup, Bjarne; Doll, J. D.; Sauerbrey, R. A.
1993-01-01
Research on optimal control of quantum systems has been severely restricted by the lack of experimentally feasible control pulses. Here, to overcome this obstacle, optimal control is considered with the help of chirped pulses. Simulated annealing is used as the optimizing procedure. The examples ...
Optimal Control Problems for Nonlinear Variational Evolution Inequalities
Directory of Open Access Journals (Sweden)
Eun-Young Ju
2013-01-01
Full Text Available We deal with optimal control problems governed by semilinear parabolic type equations and in particular described by variational inequalities. We will also characterize the optimal controls by giving necessary conditions for optimality by proving the Gâteaux differentiability of solution mapping on control variables.
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies
Abel zur Wiesch, Pia; Cohen, Ted
2017-01-01
Identifying optimal dosing of antibiotics has proven challenging—some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood. PMID:28060813
Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.
Abel Zur Wiesch, Pia; Clarelli, Fabrizio; Cohen, Ted
2017-01-01
Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.
Generalized Predictive Control for Non-Stationary Systems
DEFF Research Database (Denmark)
Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen
1994-01-01
This paper shows how the generalized predictive control (GPC) can be extended to non-stationary (time-varying) systems. If the time-variation is slow, then the classical GPC can be used in context with an adaptive estimation procedure of a time-invariant ARIMAX model. However, in this paper prior...... knowledge concerning the nature of the parameter variations is assumed available. The GPC is based on the assumption that the prediction of the system output can be expressed as a linear combination of present and future controls. Since the Diophantine equation cannot be used due to the time......-variation of the parameters, the optimal prediction is found as the general conditional expectation of the system output. The underlying model is of an ARMAX-type instead of an ARIMAX-type as in the original version of the GPC (Clarke, D. W., C. Mohtadi and P. S. Tuffs (1987). Automatica, 23, 137-148) and almost all later...
Optimal Sliding Mode Controllers for Attitude Stabilization of Flexible Spacecraft
Directory of Open Access Journals (Sweden)
Chutiphon Pukdeboon
2011-01-01
Full Text Available The robust optimal attitude control problem for a flexible spacecraft is considered. Two optimal sliding mode control laws that ensure the exponential convergence of the attitude control system are developed. Integral sliding mode control (ISMC is applied to combine the first-order sliding mode with optimal control and is used to control quaternion-based spacecraft attitude manoeuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the state-dependent Riccati equation (SDRE and optimal Lyapunov techniques are employed to solve the infinite-time nonlinear optimal control problem. The second method of Lyapunov is used to guarantee the stability of the attitude control system under the action of the proposed control laws. An example of multiaxial attitude manoeuvres is presented and simulation results are included to verify the usefulness of the developed controllers.
Control Optimization of Solar Thermally Driven Chillers
Directory of Open Access Journals (Sweden)
Antoine Dalibard
2016-10-01
Full Text Available Many installed solar thermally driven cooling systems suffer from high auxiliary electric energy consumption which makes them not more efficient than conventional compression cooling systems. A main reason for this is the use of non-efficient controls with constant set points that do not allow a chiller power modulation at partial-load and therefore lead to unnecessary high power consumption of the parasitics. The aims of this paper are to present a method to control efficiently solar thermally driven chillers, to demonstrate experimentally its applicability and to quantify the benefits. It has been shown that the cooling capacity of a diffusion absorption chiller can be modulated very effectively by adjusting both the temperature and the flow rate of the cooling water. With the developed approach and the use of optimization algorithms, both the temperature and the flow rate can be controlled simultaneously in a way that the cooling load is matched and the electricity consumption is minimized. Depending on the weather and operating conditions, electricity savings between 20% and 60% can be achieved compared to other tested control approaches. The highest savings are obtained when the chiller is operated at partial load. The presented method is not restricted to solar cooling systems and can also be applied to other conventional heating ventilation and air conditioning (HVAC systems.
DEFF Research Database (Denmark)
Jiang, Hao; Lin, Jin; Song, Yonghua;
2016-01-01
Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...
Energy Technology Data Exchange (ETDEWEB)
Huan, Ronghua; Zhu, Weiqiu [Zhejiang University, Department of Mechanics, State Key Laboratory of Fluid Power Transmission and Control, Hangzhou (China); Wu, Yongjun [East China University of Science and Technology, School of Information Science and Engineering, Shanghai (China)
2009-02-15
A new bounded optimal control strategy for multi-degree-of-freedom (MDOF) quasi nonintegrable-Hamiltonian systems with actuator saturation is proposed. First, an n-degree-of-freedom (n-DOF) controlled quasi nonintegrable-Hamiltonian system is reduced to a partially averaged Ito stochastic differential equation by using the stochastic averaging method for quasi nonintegrable-Hamiltonian systems. Then, a dynamical programming equation is established by using the stochastic dynamical programming principle, from which the optimal control law consisting of optimal unbounded control and bang-bang control is derived. Finally, the response of the optimally controlled system is predicted by solving the Fokker-Planck-Kolmogorov (FPK) equation associated with the fully averaged Ito equation. An example of two controlled nonlinearly coupled Duffing oscillators is worked out in detail. Numerical results show that the proposed control strategy has high control effectiveness and efficiency and that chattering is reduced significantly compared with the bang-bang control strategy. (orig.)
Continuous Control Artificial Potential Function Methods and Optimal Control
2014-03-27
Method, namely r̈VDSVAPF = −K̇SKR∇φ−KSK̇R∇φ−KSKRH(φ)ṙ −KD (KSKR∇φ+ ṙ) . The above dynamics are very nonlinear due to the trigonometric functions (inside...constraints (on KS and θ) and the deletion of trigonometric functions . The suspected reasons for the larger computa- tional expense are twofold. First, this...Continuous Control Artificial Potential Function Methods and Optimal Control THESIS R. Andrew Fields, Civ, USAF AFIT-ENY-14-M-20 DEPARTMENT OF THE
Predictive and Neural Predictive Control of Uncertain Systems
Kelkar, Atul G.
2000-01-01
Accomplishments and future work are:(1) Stability analysis: the work completed includes characterization of stability of receding horizon-based MPC in the setting of LQ paradigm. The current work-in-progress includes analyzing local as well as global stability of the closed-loop system under various nonlinearities; for example, actuator nonlinearities; sensor nonlinearities, and other plant nonlinearities. Actuator nonlinearities include three major types of nonlineaxities: saturation, dead-zone, and (0, 00) sector. (2) Robustness analysis: It is shown that receding horizon parameters such as input and output horizon lengths have direct effect on the robustness of the system. (3) Code development: A matlab code has been developed which can simulate various MPC formulations. The current effort is to generalize the code to include ability to handle all plant types and all MPC types. (4) Improved predictor: It is shown that MPC design using better predictors that can minimize prediction errors. It is shown analytically and numerically that Smith predictor can provide closed-loop stability under GPC operation for plants with dead times where standard optimal predictor fails. (5) Neural network predictors: When neural network is used as predictor it can be shown that neural network predicts the plant output within some finite error bound under certain conditions. Our preliminary study shows that with proper choice of update laws and network architectures such bound can be obtained. However, much work needs to be done to obtain a similar result in general case.
Nonlinear predictive control in the LHC accelerator
Blanco, E; Cristea, S; Casas, J
2009-01-01
This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation\\ maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.
On stochastic optimal control of partially observable nonlinear quasi Hamiltonian systems
Institute of Scientific and Technical Information of China (English)
朱位秋; 应祖光
2004-01-01
A stochastic optimal control strategy for partially observable nonlinear quasi Hamiltonian systems is proposed.The optimal control forces consist of two parts. The first part is determined by the conditions under which the stochastic optimal control problem of a partially observable nonlinear system is converted into that of a completely observable linear system. The second part is determined by solving the dynamical programming equation derived by applying the stochastic averaging method and stochastic dynamical programming principle to the completely observable linear control system. The response of the optimally controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation associated with the optimally controlled completely observable linear system and solving the Riccati equation for the estimated error of system states. An example is given to illustrate the procedure and effectiveness of the proposed control strategy.
Institute of Scientific and Technical Information of China (English)
朱位秋; 应祖光
2004-01-01
A stochastic optimal control strategy for partially observable nonlinear quasi Hamiltonian systems is proposed. The optimal control forces consist of two parts. The first part is determined by the conditions under which the stochastic optimal control problem of a partially observable nonlinear system is converted into that of a completely observable linear system. The second part is determined by solving the dynamical programming equation derived by applying the stochastic averaging method and stochastic dynamical programming principle to the completely observable linear control system. The response of the optimally controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation associated with the optimally controlled completely observable linear system and solving the Riccati equation for the estimated error of system states. An example is given to illustrate the procedure and effectiveness of the proposed control strategy.
Optimal localized observations for advancing beyond the ENSO predictability barrier
Directory of Open Access Journals (Sweden)
W. Kramer
2013-04-01
Full Text Available The existing 20-member ensemble of 50 yr ECHAM5/MPI-OM simulations provides a reasonably realistic Monte Carlo sample of the El Niño–Southern Oscillation (ENSO. Localized observations of sea surface temperature (SST, zonal wind speed and thermocline depth are assimilated in the ensemble using sequential importance sampling to adjust the weight of ensemble members. We determine optimal observation locations, for which assimilation yields the minimal ensemble spread. Efficient observation locations for SST lie in the ENSO pattern, with the optimum located in the eastern and western Pacific for minimizing uncertainty in the NINO3 and NINO4 index, respectively. After the assimilation of the observations, we investigate how the weighted ensemble performs as a nine-month probabilistic forecast of the ENSO. Here, we focus on the spring predictability barrier with observation in the January–March (March–May period and assess the remaining predictive power in June (August for NINO3 (NINO4. For the ECHAM5/MPI-OM ensemble, this yields that SST observations around 110° W and 140° W provide the best predictive skill for the NINO3 and NINO4 index, respectively. Forecasts can be improved by additionally measuring the thermocline depth at 150° W.
Optimally discriminative subnetwork markers predict response to chemotherapy.
Dao, Phuong; Wang, Kendric; Collins, Colin; Ester, Martin; Lapuk, Anna; Sahinalp, S Cenk
2011-07-01
Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein-protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems. We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy. The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com.
Model Predictive Control for Smart Energy Systems
DEFF Research Database (Denmark)
Halvgaard, Rasmus
load shifting capabilities of the units that adapts to the given price predictions. We furthermore evaluated control performance in terms of economic savings for different control strategies and forecasts. Chapter 5 describes and compares the proposed large-scale Aggregator control strategies....... Aggregators are assumed to play an important role in the future Smart Grid and coordinate a large portfolio of units. The developed economic MPC controllers interfaces each unit directly to an Aggregator. We developed several MPC-based aggregation strategies that coordinates the global behavior of a portfolio...
Explicit model predictive control accuracy analysis
Knyazev, Andrew; Zhu, Peizhen; Di Cairano, Stefano
2015-01-01
Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. repre...
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen N.; Sichani, Mahdi T.; Mirzaei, Mahmood
2014-01-01
The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator...... is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible....... This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller....
Distributed model predictive control made easy
Negenborn, Rudy
2014-01-01
The rapid evolution of computer science, communication, and information technology has enabled the application of control techniques to systems beyond the possibilities of control theory just a decade ago. Critical infrastructures such as electricity, water, traffic and intermodal transport networks are now in the scope of control engineers. The sheer size of such large-scale systems requires the adoption of advanced distributed control approaches. Distributed model predictive control (MPC) is one of the promising control methodologies for control of such systems. This book provides a state-of-the-art overview of distributed MPC approaches, while at the same time making clear directions of research that deserve more attention. The core and rationale of 35 approaches are carefully explained. Moreover, detailed step-by-step algorithmic descriptions of each approach are provided. These features make the book a comprehensive guide both for those seeking an introduction to distributed MPC as well as for those ...
Optimal control theory for sustainable environmental management.
Shastri, Yogendra; Diwekar, Urmila; Cabezas, Heriberto
2008-07-15
Sustainable ecosystem management aims to promote the structure and operation of the human components of the system while simultaneously ensuring the persistence of the structures and operation of the natural component. Given the complexity of this task owing to the diverse temporal and spatial scales and multidisciplinary interactions, a systems theory approach based on sound mathematical techniques is essential. Two important aspects of this approach are formulation of sustainability-based objectives and development of the management strategies. Fisher information can be used as the basis of a sustainability hypothesis to formulate relevant mathematical objectives for disparate systems, and optimal control theory provides the means to derive time-dependent management strategies. Partial correlation coefficient analysis is an efficient technique to identify the appropriate control variables for policy development. This paper represents a proof of concept for this approach using a model system that includes an ecosystem, humans, a very rudimentary industrial process, and a very simple agricultural system. Formulation and solution of the control problems help in identifying the effective management options which offer guidelines for policies in real systems. The results also emphasize that management using multiple parameters of different nature can be distinctly effective.
Numerical methods for control optimization in linear systems
Tyatyushkin, A. I.
2015-05-01
Numerical methods are considered for solving optimal control problems in linear systems, namely, terminal control problems with control and phase constraints and time-optimal control problems. Several algorithms with various computer storage requirements are proposed for solving these problems. The algorithms are intended for finding an optimal control in linear systems having certain features, for example, when the reachable set of a system has flat faces.
Generalized predictive control in the delta-domain
DEFF Research Database (Denmark)
Lauritsen, Morten Bach; Jensen, Morten Rostgaard; Poulsen, Niels Kjølstad
1995-01-01
This paper describes new approaches to generalized predictive control formulated in the delta (δ) domain. A new δ-domain version of the continuous-time emulator-based predictor is presented. It produces the optimal estimate in the deterministic case whenever the predictor order is chosen greater...... than or equal to the number of future predicted samples, however a “good” estimate is usually obtained in a much longer range of samples. This is particularly advantageous at fast sampling rates where a “conventional” predictor is bound to become very computationally demanding. Two controllers...... are considered: one having a well-defined limit as the sampling period tends to zero, the other being a close approximation to the conventional discrete-time GPC. Both algorithms are discrete in nature and well-suited for adaptive control. The fact, that δ-domain model are used does not introduce...
Economic model predictive control theory, formulations and chemical process applications
Ellis, Matthew; Christofides, Panagiotis D
2017-01-01
This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...
Robust Model Predictive Control of a Wind Turbine
DEFF Research Database (Denmark)
Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2012-01-01
In this work the problem of robust model predictive control (robust MPC) of a wind turbine in the full load region is considered. A minimax robust MPC approach is used to tackle the problem. Nonlinear dynamics of the wind turbine are derived by combining blade element momentum (BEM) theory...... and first principle modeling of the turbine flexible structure. Thereafter the nonlinear model is linearized using Taylor series expansion around system operating points. Operating points are determined by effective wind speed and an extended Kalman filter (EKF) is employed to estimate this. In addition...... of the uncertain system is employed and a norm-bounded uncertainty model is used to formulate a minimax model predictive control. The resulting optimization problem is simplified by semidefinite relaxation and the controller obtained is applied on a full complexity, high fidelity wind turbine model. Finally...
Unreachable Setpoints in Model Predictive Control
DEFF Research Database (Denmark)
Rawlings, James B.; Bonné, Dennis; Jørgensen, John Bagterp
2008-01-01
steady state is established for terminal constraint model predictive control (MPC). The region of attraction is the steerable set. Existing analysis methods for closed-loop properties of MPC are not applicable to this new formulation, and a new analysis method is developed. It is shown how to extend...
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Model predictive control for cooperative control of space robots
Kannan, Somasundar; Alamdari, Seyed Amin Sajadi; Dentler, Jan; Olivares-Mendez, Miguel A.; Voos, Holger
2017-01-01
The problem of Orbital Manipulation of Passive body is discussed here. Two scenarios including passive object rigidly attached to robotic servicers and passive body attached to servicers through manipulators are discussed. The Model Predictive Control (MPC) technique is briefly presented and successfully tested through simulations on two cases of position control of passive body in the orbit.
Sparse Packetized Predictive Control for Networked Control over Erasure Channels
DEFF Research Database (Denmark)
Nagahara, Masaaki; Quevedo, Daniel E.; Østergaard, Jan
2014-01-01
We study feedback control over erasure channels with packet-dropouts. To achieve robustness with respect to packet-dropouts, the controller transmits data packets containing plant input predictions, which minimize a finite horizon cost function. To reduce the data size of packets, we propose to a...
A Study of Electrical Motors Controlling Optimization Methods
Saeid Fatemi
2013-01-01
In order to design an efficient motor cooling system, it is important to accurately predict the power optimization which is normally dissipated in form of heat. This study presents an analytical method for estimating bearing frictional optimization and numerical method for estimating electromagnetic optimization for an electric vehicle electrical motor. The power optimization obtained use heat sources when evaluating the thermal performance of the motor. The results showed that electromagneti...
On a Highly Nonlinear Self-Obstacle Optimal Control Problem
Energy Technology Data Exchange (ETDEWEB)
Di Donato, Daniela, E-mail: daniela.didonato@unitn.it [University of Trento, Department of Mathematics (Italy); Mugnai, Dimitri, E-mail: dimitri.mugnai@unipg.it [Università di Perugia, Dipartimento di Matematica e Informatica (Italy)
2015-10-15
We consider a non-quadratic optimal control problem associated to a nonlinear elliptic variational inequality, where the obstacle is the control itself. We show that, fixed a desired profile, there exists an optimal solution which is not far from it. Detailed characterizations of the optimal solution are given, also in terms of approximating problems.
Model Predictive Control of Sewer Networks
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik; Poulsen, Niels K.; Falk, Anne K. V.
2017-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and controlled have thus become essential factors for effcient performance of waste water treatment plants. This paper examines methods for simplified modelling and controlling a sewer network. A practical approach to the problem is used by analysing simplified design model, which is based on the Barcelona benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control.
Fault Predictive Control of Compact Disk Players
DEFF Research Database (Denmark)
Odgaard, Peter Fogh; Wickerhauser, Mladen Victor
2006-01-01
Optical disc players such as CD-players have problems playing certain discs with surface faults like scratches and fingerprints. The problem is to be found in the servo controller which positions the optical pick-up, such that the laser beam is focused on the information track. A scheme handling ...... of the feature based control scheme, such that a prediction step is included. The proposed scheme is compared with the feature based control scheme, in the perspective of handling surface faults, by simulations. These simulations show the improvements given by the proposed algorithm....
Skinner Rusk unified formalism for optimal control systems and applications
Barbero-Liñán, María; Echeverría-Enríquez, Arturo; Martín de Diego, David; Muñoz-Lecanda, Miguel C.; Román-Roy, Narciso
2007-10-01
A geometric approach to time-dependent optimal control problems is proposed. This formulation is based on the Skinner and Rusk formalism for Lagrangian and Hamiltonian systems. The corresponding unified formalism developed for optimal control systems allows us to formulate geometrically the necessary conditions given by a weak form of Pontryagin's maximum principle, provided that the differentiability with respect to controls is assumed and the space of controls is open. Furthermore, our method is also valid for implicit optimal control systems and, in particular, for the so-called descriptor systems (optimal control problems including both differential and algebraic equations).
Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints
Kmet', Tibor; Kmet'ová, Mária
2009-09-01
A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.
Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
Control Methods Utilizing Energy Optimizing Schemes in Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, L.S; Thybo, C.; Stoustrup, Jakob
2003-01-01
The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives th...... the condenser pressure towards an optimal state. The objective of this is to present a feasible method that can be used for energy optimizing control. A simulation model of a simple refrigeration system will be used as basis for testing the control method.......The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives...
Optimal Power Flow Control by Rotary Power Flow Controller
Directory of Open Access Journals (Sweden)
KAZEMI, A.
2011-05-01
Full Text Available This paper presents a new power flow model for rotary power flow controller (RPFC. RPFC injects a series voltage into the transmission line and provides series compensation and phase shifting simultaneously. Therefore, it is able to control the transmission line impedance and the active power flow through it. An RPFC is composed mainly of two rotary phase shifting transformers (RPST and two conventional (series and shunt transformers. Structurally, an RPST consists of two windings (stator and rotor windings. The rotor windings of the two RPSTs are connected in parallel and their stator windings are in series. The injected voltage is proportional to the vector sum of the stator voltages and so its amplitude and angle are affected by the rotor position of the two RPSTs. This paper, describes the steady state operation and single-phase equivalent circuit of the RPFC. Also in this paper, a new power flow model, based on power injection model of flexible ac transmission system (FACTS controllers, suitable for the power flow analysis is introduced. Proposed model is used to solve optimal power flow (OPF problem in IEEE standard test systems incorporating RPFC and the optimal settings and location of the RPFC is determined.
Gerber, Brian D; Kendall, William L; Hooten, Mevin B; Dubovsky, James A; Drewien, Roderick C
2015-09-01
1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are
Health-aware Model Predictive Control of Pasteurization Plant
Karimi Pour, Fatemeh; Puig, Vicenç; Ocampo-Martinez, Carlos
2017-01-01
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
Provably Safe and Robust Learning-Based Model Predictive Control
Aswani, Anil; Sastry, S Shankar; Tomlin, Claire
2011-01-01
Controller design for systems typically faces a trade-off between robustness and performance, and the reliability of linear controllers has caused many control practitioners to focus on the former. However, there is a renewed interest in improving system performance to deal with growing energy and pollution constraints. This paper describes a learning-based model predictive control (MPC) scheme. The MPC provides deterministic guarantees on robustness and safety, and the learning is used to identify richer models of the system to improve controller performance. Our scheme uses a linear model with bounds on its uncertainty to construct invariant sets which help to provide the guarantees, and it can be generalized to other classes of models and to pseudo-spectral methods. This framework allows us to handle state and input constraints and optimize system performance with respect to a cost function. The learning occurs through the use of an oracle which returns the value and gradient of unmodeled dynamics at discr...
Nonlinear stochastic optimal bounded control of hysteretic systems with actuator saturation
Institute of Scientific and Technical Information of China (English)
Rong-hua HUAN; Wei-qiu ZHU; Yong-jun WU
2008-01-01
A modified nonlinear stochastic optimal bounded control strategy for random excited hysteretic systems with actuator saturation is proposed. First, a controlled hysteretic system is converted into an equivalent nonlinear nonhysteretic stochastic system. Then, the partially averaged It6 stochastic differential equation and dynamical programming equation are established, respectively, by using the stochastic averaging method for quasi non-integrable Hamiltonian systems and stochastic dynamical programming principle, from which the optimal control law consisting of optimal unbounded control and bang-bang control is derived. Finally, the response of optimally controlled system is predicted by solving the Fokker-Planck-Kolmogorov (FPK) equation associated with the fully averaged It6 equation. Numerical results show that the proposed control strategy has high control effectiveness and efficiency.
An Iterative Approach for Distributed Model Predictive Control of Irrigation Canals
Doan, D.; Keviczky, T.; Negenborn, R.R.; De Schutter, B.
2009-01-01
Optimization techniques have played a fundamental role in designing automatic control systems for the most part of the past half century. This dependence is ever more obvious in today’s wide-spread use of online optimization-based control methods, such as Model Predictive Control (MPC) [1]. The ability to capture process constraints and characterize comprehensive economic objective functions has made MPC the industry standard for controlling complex systems.
Dynamic optimization the calculus of variations and optimal control in economics and management
Kamien, Morton I
2012-01-01
Since its initial publication, this text has defined courses in dynamic optimization taught to economics and management science students. The two-part treatment covers the calculus of variations and optimal control. 1998 edition.
Fault Tolerant Control Using Gaussian Processes and Model Predictive Control
Directory of Open Access Journals (Sweden)
Yang Xiaoke
2015-03-01
Full Text Available Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.
Energy Coordinative Optimization of Wind-Storage-Load Microgrids Based on Short-Term Prediction
Directory of Open Access Journals (Sweden)
Changbin Hu
2015-02-01
Full Text Available According to the topological structure of wind-storage-load complementation microgrids, this paper proposes a method for energy coordinative optimization which focuses on improvement of the economic benefits of microgrids in the prediction framework. First of all, the external characteristic mathematical model of distributed generation (DG units including wind turbines and storage batteries are established according to the requirements of the actual constraints. Meanwhile, using the minimum consumption costs from the external grid as the objective function, a grey prediction model with residual modification is introduced to output the predictive wind turbine power and load at specific periods. Second, based on the basic framework of receding horizon optimization, an intelligent genetic algorithm (GA is applied to figure out the optimum solution in the predictive horizon for the complex non-linear coordination control model of microgrids. The optimum results of the GA are compared with the receding solution of mixed integer linear programming (MILP. The obtained results show that the method is a viable approach for energy coordinative optimization of microgrid systems for energy flow and reasonable schedule. The effectiveness and feasibility of the proposed method is verified by examples.
Optimality of feedback control strategies for qubit purification
Wiseman, Howard M.; Bouten, Luc
2007-01-01
Recently two papers [K. Jacobs, Phys. Rev. A {\\bf 67}, 030301(R) (2003); H. M. Wiseman and J. F. Ralph, New J. Physics {\\bf 8}, 90 (2006)] have derived control strategies for rapid purification of qubits, optimized with respect to various goals. In the former paper the proof of optimality was not mathematically rigorous, while the latter gave only heuristic arguments for optimality. In this paper we provide rigorous proofs of optimality in all cases, by applying simple concepts from optimal c...
DISOPE distributed model predictive control of cascade systems with network communication
Institute of Scientific and Technical Information of China (English)
Yan ZHANG; Shaoyuan LI
2005-01-01
A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment.Under the distributed control structure,online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication.By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained,assuming that the algorithm was convergent.This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden.The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.
Robust and optimal attitude control of spacecraft with disturbances
Park, Yonmook
2015-05-01
In this paper, a robust and optimal attitude control design that uses the Euler angles and angular velocities feedback is presented for regulation of spacecraft with disturbances. In the control design, it is assumed that the disturbance signal has the information of the system state. In addition, it is assumed that the disturbance signal tries to maximise the same performance index that the control input tries to minimise. After proposing a robust attitude control law that can stabilise the complete attitude motion of spacecraft with disturbances, the optimal attitude control problem of spacecraft is formulated as the optimal game-theoretic problem. Then it is shown that the proposed robust attitude control law is the optimal solution of the optimal game-theoretic problem. The stability of the closed-loop system for the proposed robust and optimal control law is proven by the LaSalle invariance principle. The theoretical results presented in this paper are illustrated by a numerical example.
Kumar, Ajeet
2009-01-01
We introduce a new and efficient numerical method for multicriterion optimal control and single criterion optimal control under integral constraints. The approach is based on extending the state space to include information on a "budget" remaining to satisfy each constraint; the augmented Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our approach hinges on the causality in that PDE, i.e., the monotonicity of characteristic curves in one of the newly added dimensions. A semi-Lagrangian "marching" method is used to approximate the discontinuous viscosity solution efficiently. We compare this to a recently introduced "weighted sum" based algorithm for the same problem. We illustrate our method using examples from flight path planning and robotic navigation in the presence of friendly and adversarial observers.
Directory of Open Access Journals (Sweden)
Kaijiang YU
2015-10-01
Full Text Available As the conventional control method for hybrid electric vehicle doesn’t consider the effect of known traffic light information on the vehicle energy management, this paper proposes a model predictive control intelligent optimization strategies based on traffic light information for hybrid electric vehicles. By building the simplified model of the hybrid electric vehicle and adopting the continuation/generalized minimum residual method, the model prediction problem is solved. The simulation is conducted by using MATLAB/Simulink platform. The simulation results show the effectiveness of the proposed model of the traffic light information, and that the proposed model predictive control method can improve fuel economy and the real-time control performance significantly. The research conclusions show that the proposed control strategy can achieve optimal control of the vehicle trajectory, significantly improving fuel economy of the vehicle, and meet the system requirements for the real-time optimal control.
A Multiobjective Optimization Framework for Stochastic Control of Complex Systems
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL; Maroulas, Vasileios [ORNL; Xiong, Professor Jie [The University of Tennessee
2015-01-01
This paper addresses the problem of minimizing the long-run expected average cost of a complex system consisting of subsystems that interact with each other and the environment. We treat the stochastic control problem as a multiobjective optimization problem of the one-stage expected costs of the subsystems, and we show that the control policy yielding the Pareto optimal solution is an optimal control policy that minimizes the average cost criterion for the entire system. For practical situations with constraints consistent to those we study here, our results imply that the Pareto control policy may be of value in deriving online an optimal control policy in complex systems.
Water hammer prediction and control: the Green's function method
Institute of Scientific and Technical Information of China (English)
Li-Jun Xuan; Feng Mao; Jie-Zhi Wu
2012-01-01
By Green's function method we show that the water hammer (WH) can be analytically predicted for both laminar and turbulent flows (for the latter,with an eddy viscosity depending solely on the space coordinates),and thus its hazardous effect can be rationally controlled and minimized.To this end,we generalize a laminar water hammer equation of Wang et al.(J.Hydrodynamics,B2,51,1995)to include arbitrary initial condition and variable viscosity,and obtain its solution by Green's function method.The predicted characteristic WH behaviors by the solutions are in excellent agreement with both direct numerical simulation of the original governing equations and,by adjusting the eddy viscosity coefficient,experimentally measured turbulent flow data.Optimal WH control principle is thereby constructed and demonstrated.
Predictive powertrain control using powertrain history and GPS data
Weslati, Feisel; Krupadanam, Ashish A
2015-03-03
A method and powertrain apparatus that predicts a route of travel for a vehicle and uses historical powertrain loads and speeds for the predicted route of travel to optimize at least one powertrain operation for the vehicle.
Model predictive control of MSMPR crystallizers
Moldoványi, Nóra; Lakatos, Béla G.; Szeifert, Ferenc
2005-02-01
A multi-input-multi-output (MIMO) control problem of isothermal continuous crystallizers is addressed in order to create an adequate model-based control system. The moment equation model of mixed suspension, mixed product removal (MSMPR) crystallizers that forms a dynamical system is used, the state of which is represented by the vector of six variables: the first four leading moments of the crystal size, solute concentration and solvent concentration. Hence, the time evolution of the system occurs in a bounded region of the six-dimensional phase space. The controlled variables are the mean size of the grain; the crystal size-distribution and the manipulated variables are the input concentration of the solute and the flow rate. The controllability and observability as well as the coupling between the inputs and the outputs was analyzed by simulation using the linearized model. It is shown that the crystallizer is a nonlinear MIMO system with strong coupling between the state variables. Considering the possibilities of the model reduction, a third-order model was found quite adequate for the model estimation in model predictive control (MPC). The mean crystal size and the variance of the size distribution can be nearly separately controlled by the residence time and the inlet solute concentration, respectively. By seeding, the controllability of the crystallizer increases significantly, and the overshoots and the oscillations become smaller. The results of the controlling study have shown that the linear MPC is an adaptable and feasible controller of continuous crystallizers.
Web malware spread modelling and optimal control strategies
Liu, Wanping; Zhong, Shouming
2017-02-01
The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.
Mechanical design and optimal control of humanoid robot (TPinokio
Directory of Open Access Journals (Sweden)
Teck Chew Wee
2014-04-01
Full Text Available The mechanical structure and the control of the locomotion of bipedal humanoid is an important and challenging domain of research in bipedal robots. Accurate models of the kinematics and dynamics of the robot are essential to achieve bipedal locomotion. Toe-foot walking produces a more natural and faster walking speed and it is even possible to perform stretch knee walking. This study presents the mechanical design of a toe-feet bipedal, TPinokio and the implementation of some optimal walking gait generation methods. The optimality in the gait trajectory is achieved by applying augmented model predictive control method and the pole-zero cancellation method, taken into consideration of a trade-off between walking speed and stability. The mechanism of the TPinokio robot is designed in modular form, so that its kinematics can be modelled accurately into a multiple point-mass system, its dynamics is modelled using the single and double mass inverted pendulum model and zero-moment-point concept. The effectiveness of the design and control technique is validated by simulation testing with the robot walking on flat surface and climbing stairs.
Optimization and Control of Pressure Swing Adsorption Processes Under Uncertainty
Khajuria, Harish
2012-03-21
The real-time periodic performance of a pressure swing adsorption (PSA) system strongly depends on the choice of key decision variables and operational considerations such as processing steps and column pressure temporal profiles, making its design and operation a challenging task. This work presents a detailed optimization-based approach for simultaneously incorporating PSA design, operational, and control aspects under the effect of time variant and invariant disturbances. It is applied to a two-bed, six-step PSA system represented by a rigorous mathematical model, where the key optimization objective is to maximize the expected H2 recovery while achieving a closed loop product H2 purity of 99.99%, for separating 70% H2, 30% CH4 feed. The benefits over sequential design and control approach are shown in terms of closed-loop recovery improvement of more than 3%, while the incorporation of explicit/multiparametric model predictive controllers improves the closed loop performance. © 2012 American Institute of Chemical Engineers (AIChE).
Mesh refinement strategy for optimal control problems
Paiva, L. T.; Fontes, F. A. C. C.
2013-10-01
Direct methods are becoming the most used technique to solve nonlinear optimal control problems. Regular time meshes having equidistant spacing are frequently used. However, in some cases these meshes cannot cope accurately with nonlinear behavior. One way to improve the solution is to select a new mesh with a greater number of nodes. Another way, involves adaptive mesh refinement. In this case, the mesh nodes have non equidistant spacing which allow a non uniform nodes collocation. In the method presented in this paper, a time mesh refinement strategy based on the local error is developed. After computing a solution in a coarse mesh, the local error is evaluated, which gives information about the subintervals of time domain where refinement is needed. This procedure is repeated until the local error reaches a user-specified threshold. The technique is applied to solve the car-like vehicle problem aiming minimum consumption. The approach developed in this paper leads to results with greater accuracy and yet with lower overall computational time as compared to using a time meshes having equidistant spacing.
Optimal control theory for unitary transformations
Palao, J P; Palao, Jose P.
2003-01-01
The dynamics of a quantum system driven by an external field is well described by a unitary transformation generated by a time dependent Hamiltonian. The inverse problem of finding the field that generates a specific unitary transformation is the subject of study. The unitary transformation which can represent an algorithm in a quantum computation is imposed on a subset of quantum states embedded in a larger Hilbert space. Optimal control theory (OCT) is used to solve the inversion problem irrespective of the initial input state. A unified formalism, based on the Krotov method is developed leading to a new scheme. The schemes are compared for the inversion of a two-qubit Fourier transform using as registers the vibrational levels of the $X^1\\Sigma^+_g$ electronic state of Na$_2$. Raman-like transitions through the $A^1\\Sigma^+_u$ electronic state induce the transitions. Light fields are found that are able to implement the Fourier transform within a picosecond time scale. Such fields can be obtained by pulse-...
Predictive access control for distributed computation
DEFF Research Database (Denmark)
Yang, Fan; Hankin, Chris; Nielson, Flemming
2013-01-01
We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies — policies based on the future...... behavior of a program. A novel feature of our approach is that we can define policies concerning secondary use of data....
Directory of Open Access Journals (Sweden)
César Hernández-Hernández
2017-06-01
Full Text Available Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.
Directory of Open Access Journals (Sweden)
Weifeng Wang
2014-01-01
Full Text Available We study an optimal control problem governed by a semilinear parabolic equation, whose control variable is contained only in the boundary condition. An existence theorem for the optimal control is obtained.
ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms
Directory of Open Access Journals (Sweden)
Jin Woo Moon
2015-09-01
Full Text Available This study aimed at developing an artificial-neural-network (ANN-based model that can calculate the required time for restoring the current indoor temperature during the setback period in accommodation buildings to the normal set-point temperature in the cooling season. By applying the calculated time in the control logic, the operation of the cooling system can be predetermined to condition the indoor temperature comfortably in a more energy-efficient manner. Three major steps employing the numerical computer simulation method were conducted for developing an ANN model and testing its prediction performance. In the development process, the initial ANN model was determined to have input neurons that had a significant statistical relationship with the output neuron. In addition, the structure of the ANN model and learning methods were optimized through the parametrical analysis of the prediction performance. Finally, through the performance tests in terms of prediction accuracy, the optimized ANN model presented a lower mean biased error (MBE rate between the simulation and prediction results under generally accepted levels. Thus, the developed ANN model was proven to have the potential to be applied to thermal control logic.
A Modified Model Predictive Control Scheme
Institute of Scientific and Technical Information of China (English)
Xiao-Bing Hu; Wen-Hua Chen
2005-01-01
In implementations of MPC (Model Predictive Control) schemes, two issues need to be addressed. One is how to enlarge the stability region as much as possible. The other is how to guarantee stability when a computational time limitation exists. In this paper, a modified MPC scheme for constrained linear systems is described. An offline LMI-based iteration process is introduced to expand the stability region. At the same time, a database of feasible control sequences is generated offline so that stability can still be guaranteed in the case of computational time limitations. Simulation results illustrate the effectiveness of this new approach.
Predictive IP controller for robust position control of linear servo system.
Lu, Shaowu; Zhou, Fengxing; Ma, Yajie; Tang, Xiaoqi
2016-07-01
Position control is a typical application of linear servo system. In this paper, to reduce the system overshoot, an integral plus proportional (IP) controller is used in the position control implementation. To further improve the control performance, a gain-tuning IP controller based on a generalized predictive control (GPC) law is proposed. Firstly, to represent the dynamics of the position loop, a second-order linear model is used and its model parameters are estimated on-line by using a recursive least squares method. Secondly, based on the GPC law, an optimal control sequence is obtained by using receding horizon, then directly supplies the IP controller with the corresponding control parameters in the real operations. Finally, simulation and experimental results are presented to show the efficiency of proposed scheme.
Optimal Control Of Nonlinear Wave Energy Point Converters
DEFF Research Database (Denmark)
Nielsen, Søren R.K.; Zhou, Qiang; Kramer, Morten
2013-01-01
In this paper the optimal control law for a single nonlinear point absorber in irregular sea-states is derived, and proven to be a closed-loop controller with feedback from measured displacement, velocity and acceleration of the floater. However, a non-causal integral control component dependent...... idea behind the control strategy is to enforce the stationary velocity response of the absorber into phase with the wave excitation force at any time. The controller is optimal under monochromatic wave excitation. It is demonstrated that the devised causal controller, in plane irregular sea states......, absorbs almost the same power as the optimal controller....
Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Dong-yun Wang
2013-01-01
Full Text Available Optimal control using particle swarm optimization (PSO is put forward in a parallel hybrid hydraulic excavator (PHHE. A power-train mathematical model of PHHE is illustrated along with the analysis of components’ parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rule-based one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is off-line optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators.
Aircraft nonlinear optimal control using fuzzy gain scheduling
Nusyirwan, I. F.; Kung, Z. Y.
2016-10-01
Fuzzy gain scheduling is a common solution for nonlinear flight control. The highly nonlinear region of flight dynamics is determined throughout the examination of eigenvalues and the irregular pattern of root locus plots that show the nonlinear characteristic. By using the optimal control for command tracking, the pitch rate stability augmented system is constructed and the longitudinal flight control system is established. The outputs of optimal control for 21 linear systems are fed into the fuzzy gain scheduler. This research explores the capability in using both optimal control and fuzzy gain scheduling to improve the efficiency in finding the optimal control gains and to achieve Level 1 flying qualities. The numerical simulation work is carried out to determine the effectiveness and performance of the entire flight control system. The simulation results show that the fuzzy gain scheduling technique is able to perform in real time to find near optimal control law in various flying conditions.
Presentation of Malaria Epidemics Using Multiple Optimal Controls
Directory of Open Access Journals (Sweden)
Abid Ali Lashari
2012-01-01
Full Text Available An existing model is extended to assess the impact of some antimalaria control measures, by re-formulating the model as an optimal control problem. This paper investigates the fundamental role of three type of controls, personal protection, treatment, and mosquito reduction strategies in controlling the malaria. We work in the nonlinear optimal control framework. The existence and the uniqueness results of the solution are discussed. A characterization of the optimal control via adjoint variables is established. The optimality system is solved numerically by a competitive Gauss-Seidel-like implicit difference method. Finally, numerical simulations of the optimal control problem, using a set of reasonable parameter values, are carried out to investigate the effectiveness of the proposed control measures.
Control Methods Utilizing Energy Optimizing Schemes in Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, L.S; Thybo, C.; Stoustrup, Jakob;
2003-01-01
The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives...
Predictive Process Optimization for Fracture Ductility in Automotive TRIP Steels
Gong, Jiadong
stabilizing aging treatment was proposed to address the enhancement of hole-expansion ductility in this class of steel, through optimization of retained austenite stability for transformation toughening. Using a commercial galvannealed TRIP steel of 800 MPa strength level as an example, the paraequilibrium model was established with carbon partition stored energy fitted with experimental data and applied in the thermodynamic simulations by Thermo-CalcRTM and DICTRARTM. It is found the specimens aged at 447 °C for 2 minutes demonstrate evident improvement in the fracture ductility compared with original as-received galvannealed TRIP steel. The true fracture strain grows from 0.45 to 0.71, associated with a reduction of Ms sigma from 70 °C to 15 °C. Further application of the computational design model on a series of similar TRIP steels confirms the effect of the stability optimization on enhancing the hole-expansion ratio by ˜ 30% on average. The optimum processing parameters along with the new stabilizing aging heat-treatment was predictively designed to successfully improve the material meeting the objectives.
Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle
Directory of Open Access Journals (Sweden)
Huei Peng
2012-11-01
Full Text Available The optimal sizing and control of a hybrid tracked vehicle is presented and solved in this paper. A driving schedule obtained from field tests is used to represent typical tracked vehicle operations. Dynamics of the diesel engine-permanent magnetic AC synchronous generator set, the lithium-ion battery pack, and the power split between them are modeled and validated through experiments. Two coupled optimizations, one for the plant parameters, forming the outer optimization loop and one for the control strategy, forming the inner optimization loop, are used to achieve minimum fuel consumption under the selected driving schedule. The dynamic programming technique is applied to find the optimal controller in the inner loop while the component parameters are optimized iteratively in the outer loop. The results are analyzed, and the relationship between the key parameters is observed to keep the optimal sizing and control simultaneously.
Optimization and control methods in industrial engineering and construction
Wang, Xiangyu
2014-01-01
This book presents recent advances in optimization and control methods with applications to industrial engineering and construction management. It consists of 15 chapters authored by recognized experts in a variety of fields including control and operation research, industrial engineering, and project management. Topics include numerical methods in unconstrained optimization, robust optimal control problems, set splitting problems, optimum confidence interval analysis, a monitoring networks optimization survey, distributed fault detection, nonferrous industrial optimization approaches, neural networks in traffic flows, economic scheduling of CCHP systems, a project scheduling optimization survey, lean and agile construction project management, practical construction projects in Hong Kong, dynamic project management, production control in PC4P, and target contracts optimization. The book offers a valuable reference work for scientists, engineers, researchers and practitioners in industrial engineering and c...
Optimal control of a fed-batch fermentation
Energy Technology Data Exchange (ETDEWEB)
Dekkers, R.M.
1984-01-01
The common cultivation of bakers' yeast is an aerobic fed-batch fermentation under sugar-limited growth. The ultimate objective of on-line computer control is to optimize the process through maximizing the productivity of biomass formation while minimizing the consumption of raw materials for the product. Results obtained on the optimal control of a fed-batch fermentation are given. The aspects to be considered are instrumentation, state estimation, optimization and process control.
Model Predictive Control of Buoy Type Wave Energy Converter
DEFF Research Database (Denmark)
Soltani, Mohsen; Sichani, Mahdi Teimouri; Mirzaei, Mahmood
2014-01-01
The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator is an a....... This approach is then taken into account and an MPC controller is designed for a model WEC and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller.......The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator...... is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible...
Dynamic consistency for Stochastic Optimal Control problems
Carpentier, Pierre; Cohen, Guy; De Lara, Michel; Girardeau, Pierre
2010-01-01
For a sequence of dynamic optimization problems, we aim at discussing a notion of consistency over time. This notion can be informally introduced as follows. At the very first time step $t_0$, the decision maker formulates an optimization problem that yields optimal decision rules for all the forthcoming time step $t_0, t_1, ..., T$; at the next time step $t_1$, he is able to formulate a new optimization problem starting at time $t_1$ that yields a new sequence of optimal decision rules. This process can be continued until final time $T$ is reached. A family of optimization problems formulated in this way is said to be time consistent if the optimal strategies obtained when solving the original problem remain optimal for all subsequent problems. The notion of time consistency, well-known in the field of Economics, has been recently introduced in the context of risk measures, notably by Artzner et al. (2007) and studied in the Stochastic Programming framework by Shapiro (2009) and for Markov Decision Processes...
Model Predictive Control for the Operation of Building Cooling Systems
Energy Technology Data Exchange (ETDEWEB)
Ma, Yudong; Borrelli, Francesco; Hencey, Brandon; Coffey, Brian; Bengea, Sorin; Haves, Philip
2010-06-29
A model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reduction in the central plant electricity cost and improvement of its efficiency.
Optimally Controlled Flexible Fuel Powertrain System
Energy Technology Data Exchange (ETDEWEB)
Duncan Sheppard; Bruce Woodrow; Paul Kilmurray; Simon Thwaite
2011-06-30
) developed the Renewable Fuels Standard (RFS) under the Energy Policy Act of 2005. The RFS specifies targets for the amount of renewable fuel to be blended into petroleum based transportation fuels. The goal is to blend 36 billion gallons of renewable fuels into transportation fuels by 2022 (9 billion gallons were blended in 2008). The RFS also requires that the renewable fuels emit fewer greenhouse gasses than the petroleum fuels replaced. Thus the goal of the EPA is to have a more fuel efficient national fleet, less dependent on petroleum based fuels. The limit to the implementation of certain technologies employed was the requirement to run the developed powertrain on gasoline with minimal performance degradation. The addition of ethanol to gasoline fuels improves the fuels octane rating and increases the fuels evaporative cooling. Both of these fuel property enhancements make gasoline / ethanol blends more suitable than straight gasoline for use in downsized engines or engines with increased compression ratio. The use of engine downsizing and high compression ratios as well as direct injection (DI), dual independent cam phasing, external EGR, and downspeeding were fundamental to the fuel economy improvements targeted in this project. The developed powertrain specification utilized the MAHLE DI3 gasoline downsizing research engine. It was a turbocharged, intercooled, DI engine with dual independent cam phasing utilizing a compression ratio of 11.25 : 1 and a 15% reduction in final drive ratio. When compared to a gasoline fuelled 2.2L Ecotec engine in a Chevrolet HHR, vehicle drive cycle predictions indicate that the optimized powertrain operating on E85 would result in a reduced volume based drive cycle fuel economy penalty of 6% compared to an approximately 30% penalty for current technology engines.
TF/TA2 trajectory tracking using nonlinear predictive control approach
Institute of Scientific and Technical Information of China (English)
Tang Qiang; Zhang Xinguo; Liu Xicheng
2006-01-01
The use of a methodology of nonlinear continuous predictive control to design the guidance control law for the aircraft TF/TA2 trajectory tracking problem is emplojed. For the derivation of the predictive control law, by using Taylor series expansion, and based on optimizing a performance index which is a quadratic function of both the predictive value of the state variables and the control inputs, a state variable feedback controller for nonlinear systems is obtained, and it provides a tradeoff between satisfactory tracking performance and the control magnitude requirements. Numerical simulation results for a supersonic fighter aircraft model show the viability of this approach.
Application of predictive control scheduling method to real-time periodic control tasks overrun
Institute of Scientific and Technical Information of China (English)
SHEN Qing; GUI Wei-hua; YANG Chun-hua; YANG Tie-jun
2007-01-01
Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.
Calculus of variations and optimal control theory a concise introduction
Liberzon, Daniel
2011-01-01
This textbook offers a concise yet rigorous introduction to calculus of variations and optimal control theory, and is a self-contained resource for graduate students in engineering, applied mathematics, and related subjects. Designed specifically for a one-semester course, the book begins with calculus of variations, preparing the ground for optimal control. It then gives a complete proof of the maximum principle and covers key topics such as the Hamilton-Jacobi-Bellman theory of dynamic programming and linear-quadratic optimal control. Calculus of Variations and Optimal Control Theory
Constrained time-optimal control of double-integrator system and its application in MPC
Fehér, Marek; Straka, Ondřej; Šmídl, Václav
2017-01-01
The paper deals with the design of a time-optimal controller for systems subject to both state and control constraints. The focus is laid on a double-integrator system, for which the time-to-go function is calculated. The function is then used as a part of a model predictive control criterion where it represents the long-horizon part. The designed model predictive control algorithm is then used in a constrained control problem of permanent magnet synchronous motor model, which behavior can be approximated by a double integrator model. Accomplishments of the control goals are illustrated in a numerical example.
Near-Optimal Algorithms for Online Matrix Prediction
Hazan, Elad; Shalev-Shwartz, Shai
2012-01-01
In several online prediction problems of recent interest the comparison class is composed of matrices with bounded entries. For example, in the online max-cut problem, the comparison class is matrices which represent cuts of a given graph and in online gambling the comparison class is matrices which represent permutations over n teams. Another important example is online collaborative filtering in which a widely used comparison class is the set of matrices with a small trace norm. In this paper we isolate a property of matrices, which we call (beta,tau)-decomposability, and derive an efficient online learning algorithm, that enjoys a regret bound of O*(sqrt(beta tau T)) for all problems in which the comparison class is composed of (beta,tau)-decomposable matrices. By analyzing the decomposability of cut matrices, triangular matrices, and low trace-norm matrices, we derive near optimal regret bounds for online max-cut, online gambling, and online collaborative filtering. In particular, this resolves (in the af...
Optimal prediction for moment models: Crescendo diffusion and reordered equations
Seibold, Benjamin
2009-01-01
A direct numerical solution of the radiative transfer equation or any kinetic equation is typically expensive, since the radiative intensity depends on time, space and direction. An expansion in the direction variables yields an equivalent system of infinitely many moments. A fundamental problem is how to truncate the system. Various closures have been presented in the literature. We want to study moment closure generally within the framework of optimal prediction, a strategy to approximate the mean solution of a large system by a smaller system, for radiation moment systems. We apply this strategy to radiative transfer and show that several closures can be re-derived within this framework, e.g. $P_N$, diffusion, and diffusion correction closures. In addition, the formalism gives rise to new parabolic systems, the reordered $P_N$ equations, that are similar to the simplified $P_N$ equations. Furthermore, we propose a modification to existing closures. Although simple and with no extra cost, this newly derived...
Optimal prediction for moment models: crescendo diffusion and reordered equations
Seibold, Benjamin; Frank, Martin
2009-12-01
A direct numerical solution of the radiative transfer equation or any kinetic equation is typically expensive, since the radiative intensity depends on time, space and direction. An expansion in the direction variables yields an equivalent system of infinitely many moments. A fundamental problem is how to truncate the system. Various closures have been presented in the literature. We want to generally study the moment closure within the framework of optimal prediction, a strategy to approximate the mean solution of a large system by a smaller system, for radiation moment systems. We apply this strategy to radiative transfer and show that several closures can be re-derived within this framework, such as P N , diffusion, and diffusion correction closures. In addition, the formalism gives rise to new parabolic systems, the reordered P N equations, that are similar to the simplified P N equations. Furthermore, we propose a modification to existing closures. Although simple and with no extra cost, this newly derived crescendo diffusion yields better approximations in numerical tests.
Ensemble prediction experiments using conditional nonlinear optimal perturbation
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from December to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.
Ensemble prediction experiments using conditional nonlinear optimal perturbation
Institute of Scientific and Technical Information of China (English)
JIANG ZhiNa; MU Mu; WANG DongHai
2009-01-01
Two methods for initialization of ensemble forecasts are compared, namely, singular vector (SV) and conditional nonlinear optimal perturbation (CNOP). The comparison is done for forecast lengths of up to 10 days with a three-level quasi-geostrophic (QG) atmospheric model in a perfect model scenario. Ten cases are randomly selected from 1982/1983 winter to 1993/1994 winter (from 12 to the following February). Anomaly correlation coefficient (ACC) is adopted as a tool to measure the quality of the predicted ensembles on the Northern Hemisphere 500 hPa geopotential height. The results show that the forecast quality of ensemble samples in which the first SV is replaced by CNOP is higher than that of samples composed of only SVs in the medium range, based on the occurrence of weather re-gime transitions in Northern Hemisphere after about four days. Besides, the reliability of ensemble forecasts is evaluated by the Rank Histograms. The above conclusions confirm .and extend those reached earlier by the authors, which stated that the introduction of CNOP improves the forecast skill under the condition that the analysis error belongs to a kind of fast-growing error by using a barotropic QG model.
Sopov, E.; Semenkina, O.
2015-01-01
Genetic and distribution building algorithms with binary representation are analyzed. A property of convergence to the optimal solution is discussed. A novel convergence prediction method is proposed and investigated. The method is based on analysis of gene value probabilities distribution dynamics, thus it can predict gene values of the optimal solution to which the algorithm converges. The results of investigations for the optimal prediction algorithm performance are presented.
Wind farms production: Control and prediction
El-Fouly, Tarek Hussein Mostafa
Wind energy resources, unlike dispatchable central station generation, produce power dependable on external irregular source and that is the incident wind speed which does not always blow when electricity is needed. This results in the variability, unpredictability, and uncertainty of wind resources. Therefore, the integration of wind facilities to utility electrical grid presents a major challenge to power system operator. Such integration has significant impact on the optimum power flow, transmission congestion, power quality issues, system stability, load dispatch, and economic analysis. Due to the irregular nature of wind power production, accurate prediction represents the major challenge to power system operators. Therefore, in this thesis two novel models are proposed for wind speed and wind power prediction. One proposed model is dedicated to short-term prediction (one-hour ahead) and the other involves medium term prediction (one-day ahead). The accuracy of the proposed models is revealed by comparing their results with the corresponding values of a reference prediction model referred to as the persistent model. Utility grid operation is not only impacted by the uncertainty of the future production of wind farms, but also by the variability of their current production and how the active and reactive power exchange with the grid is controlled. To address this particular task, a control technique for wind turbines, driven by doubly-fed induction generators (DFIGs), is developed to regulate the terminal voltage by equally sharing the generated/absorbed reactive power between the rotor-side and the gridside converters. To highlight the impact of the new developed technique in reducing the power loss in the generator set, an economic analysis is carried out. Moreover, a new aggregated model for wind farms is proposed that accounts for the irregularity of the incident wind distribution throughout the farm layout. Specifically, this model includes the wake effect
A mathematical formulation for optimal control of air pollution
Institute of Scientific and Technical Information of China (English)
朱江; 曾庆存
2003-01-01
The problem of optimal control of air pollution using weather forecastresults and numerical air pollution models is discussed. A mathematical formulation of the problem is presented. The control is an act on pollution sources with feasible constraints. Based on forecasted weather conditions, the objective ofthe optimal control is to minimize total cost caused by control under the constraint that the pollution concentrations over a certain period and a certain spatial domain are less than some specified values. Using the adjoint method, an effective algorithm is given. Since the optimal solutions are based on weather forecasts, the errors in weather forecasts will cause uncertainties in the optimal solutions. Estimation of impacts of weather forecast errors on the optimal solutions is discussed using the adjoint sensitivity analysis technique that is an approximated, however very effective method. The adjoint sensitivity analysis technique can be used to calculate the impacts of errors in wind, temperature and initial pollutant concentration fields on performances of the optimal control.
Optimal parametric sensitivity control for a fed-batch reactor
Stigter, J.D.; Keesman, K.J.
2001-01-01
The paper presents a method to derive an optimal parametric sensitivity controller for optimal estimation of a set of parameters in an experiment. The method is demonstrated for a fed batch bio-reactor case study for optimal estimation of the saturation constant Ks and, albeit intuitively, the param
Power prediction in mobile communication systems using an optimal neural-network structure.
Gao, X M; Gao, X Z; Tanskanen, J A; Ovaska, S J
1997-01-01
Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.
Minimum energy control and optimal-satisfactory control of Boolean control network
Energy Technology Data Exchange (ETDEWEB)
Li, Fangfei, E-mail: li_fangfei@163.com; Lu, Xiwen
2013-12-09
In the literatures, to transfer the Boolean control network from the initial state to the desired state, the expenditure of energy has been rarely considered. Motivated by this, this Letter investigates the minimum energy control and optimal-satisfactory control of Boolean control network. Based on the semi-tensor product of matrices and Floyd's algorithm, minimum energy, constrained minimum energy and optimal-satisfactory control design for Boolean control network are given respectively. A numerical example is presented to illustrate the efficiency of the obtained results.
Using Lyapunov function to design optimal controller for AQM routers
Institute of Scientific and Technical Information of China (English)
ZHANG Peng; YE Cheng-qing; MA Xue-ying; CHEN Yan-hua; LI Xin
2007-01-01
It was shown that active queue management schemes implemented in the routers of communication networks supporting transmission control protocol (TCP) flows can be modelled as a feedback control system. In this paper based on Lyapunov function we developed an optimal controller to improve active queue management (AQM) router's stability and response time,which are often in conflict with each other in system performance. Ns-2 simulations showed that optimal controller outperforms PI controller significantly.
Institute of Scientific and Technical Information of China (English)
LI Xi; DENG Zhong-hua; CAO Guang-yi; ZHU Xin-jian; WEI Dong
2006-01-01
Proton exchange membrane fuel cell (PEMFC) stack temperature and cathode stoichiometric oxygen are very important control parameters. The performance and lifespan of PEMFC stack are greatly dependent on the parameters. So, in order to improve the performance index, tight control of two parameters within a given range and reducing their fluctuation are indispensable.However, control-oriented models and control strategies are very weak junctures in the PEMFC development. A predictive control algorithm was presented based on their model established by input-output data and operating experiences. It adjusts the operating temperature to 80 ℃. At the same time, the optimized region of stoichiometric oxygen is kept between 1.8-2.2. Furthermore, the control algorithm adjusts the variants quickly to the destination value and makes the fluctuation of the variants the least. According to the test results, compared with traditional fuzzy and PID controllers, the designed controller shows much better performance.
Directory of Open Access Journals (Sweden)
Ruisheng Sun
2016-01-01
Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.
Using Chemicals to Optimize Conformance Control in Fractured Reservoirs
Energy Technology Data Exchange (ETDEWEB)
Seright, Randall; Liang, Jenn-Tai; Schrader, Richard; Hagstrom II, John; Wang, Ying; Kumar, Anand; Wavrik, Kathryn
2001-09-07
The objectives of this project are: (1) to develop a capability to predict and optimize the ability of gels to reduce permeability to water more than that to oil or gas, (2) to develop procedures for optimizing blocking agent placement in wells where hydraulic fractures cause channeling problems, and (3) to develop procedures to optimize blocking agent placement in naturally fractured reservoirs. Work was directed at both injection wells and production wells and at vertical, horizontal, and highly deviated wells.
Improved Robustness of Generalized Predictive Control for Uncertain Systems
Khelifa, Khelifi Otmane; Noureddine, Bali; Lazhari, Nezli
2015-01-01
An off-line methodology has been developed to improve the robustness of an initial generalized predictive control (GPC) through convex optimization of the Youla parameter. However, this method is restricted with the case of the systems affected only by unstructured uncertainties. This paper proposes an extension of this method to the systems subjected to both unstructured and polytopic uncertainties. The basic idea consists in adding supplementary constraints to the optimization problem which validates the Lipatov stability condition at each vertex of the polytope. These polytopic uncertainties impose a non convex quadratically constrained quadratic programming (QCQP) problem. Based on semidefinite programming (SDP), this problem is relaxed and solved. Therefore, the robustification provides stability robustness towards unstructured uncertainties for the nominal system, while guaranteeing stability properties over a specified polytopic domain of uncertainties. Finally, we present a numerical example to demonstrate the proposed method.
Directory of Open Access Journals (Sweden)
Abdul Wahid
2016-02-01
Full Text Available Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO or multi-variable system, which is hard to be controlled. Model predictive control (MPC is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s, prediction horizon (P = 1-400, and the control horizon (M=1-400. The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller.
Optimal Control of Airfoil Flow Separation using Fluidic Excitation
Shahrabi, Arireza F.
as well as F+ were evaluated and discussed. The computational model predictions showed good agreement with the experimental data. It was observed that different angles of attack and flap angles have different requirements for the minimum value of the momentum coefficient, Cμ, in order for the SJA to be effective for control of separation. It was also found that the variation of F + noticeably affects the lift and drag forces acting on the airfoil. The optimum values of parameters during open loop control simulations have been applied in order to introduce the optimal open loop control outcome. An innovative approach has been implemented to formulate optimal frequencies and momentum ratios of vortex shedding which depends on angle of attack and static pressure of the separation zone in the upper chord. Optimal open loop results have been compared with the optimal closed loop results. Cumulative case studies in the matter of angle of attacks, flap angles, Re, Cμ and F+ provide a convincing collection of evidence to the following conclusion. An improvement of a direct closed loop control was demonstrated, and an analytical formula describing the properties of a separated flow and vortex shedding was proposed. Best AFC solutions are offered by providing optimal frequencies and momentum ratios at a variety of flow conditions.
Hierarchical control based on Hopfield network for nonseparable optimization problems
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
The nonseparable optimization control problem is considered, where the overall objective function is not of an additive form with respect to subsystems. Since there exists the problem that computation is very slow when using iterative algorithms in multiobjective optimization, Hopfield optimization hierarchical network based on IPM is presented to overcome such slow computation difficulty. Asymptotic stability of this Hopfield network is proved and its equilibrium point is the optimal point of the original problem. The simulation shows that the net is effective to deal with the optimization control problem for large-scale nonseparable steady state systems.
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
A discrete-time hybrid model of a permanent magnet synchronous motor (PMSM) with saturation in voltage and current is formulated.The controller design with incorporated constraints is achieved in a systematic way from modeling to control synthesis and implementation.The Hybrid System Description Language is used to obtain a mixed-logical dynamical (MLD) model.Based on the MLD model,a model predictive controller is designed for an optimal speed regulation of the motor.For reducing computation complexity and ...
Passive Motion Paradigm: an alternative to Optimal Control
Directory of Open Access Journals (Sweden)
Vishwanathan eMohan
2011-12-01
Full Text Available In the last years, optimal control theory (OCT has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioural neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the ‘degrees of freedom problem’, the common core of production, observation, reasoning, and learning of ‘actions’. OCT, directly derived from engineering design techniques of control systems quantifies task goals as ‘cost functions’ and uses the sophisticated formal tools of optimal control to obtain desired behaviour (and predictions. We propose an alternative ‘softer’ approach (PMP: Passive Motion Paradigm that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt and overt are the consequences of an internal simulation process that ‘animates’ the body schema with the attractor dynamics of force fields induced by the goal and task specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task oriented constraints ‘at runtime’, hence solving the ‘degrees of freedom problem’ without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only to shape motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of ‘potential actions’. In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory, mirror neurons and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it
Passive Motion Paradigm: An Alternative to Optimal Control
Mohan, Vishwanathan; Morasso, Pietro
2011-01-01
In the last years, optimal control theory (OCT) has emerged as the leading approach for investigating neural control of movement and motor cognition for two complementary research lines: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the “degrees of freedom (DoFs) problem,” the common core of production, observation, reasoning, and learning of “actions.” OCT, directly derived from engineering design techniques of control systems quantifies task goals as “cost functions” and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative “softer” approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that “animates” the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints “at runtime,” hence solving the “DoFs problem” without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of “potential actions.” In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of
H2-optimal control with generalized state-space models for use in control-structure optimization
Wette, Matt
1991-01-01
Several advances are provided solving combined control-structure optimization problems. The author has extended solutions from H2 optimal control theory to the use of generalized state space models. The generalized state space models preserve the sparsity inherent in finite element models and hence provide some promise for handling very large problems. Also, expressions for the gradient of the optimal control cost are derived which use the generalized state space models.
Study on optimization control method based on artificial neural network
Institute of Scientific and Technical Information of China (English)
FU Hua; SUN Shao-guang; XU Zhen-Iiang
2005-01-01
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
Prediction control of active power filters
Institute of Scientific and Technical Information of China (English)
王莉娜; 罗安
2003-01-01
A prediction method to obtain harmonic reference for active power filter is presented. It is a new use ofthe adaptive predictive filter based on FIR. The delay inherent in digital controller is successfully compensated by u-sing the proposed method, and the computing load is not very large compared with the conventional method. Moreo-ver, no additional hardware is needed. Its DSP-based realization is also presented, which is characterized by time-va-riant rate sampling, quasi synchronous sampling, and synchronous operation among the line frequency, PWM gener-ating and sampling in A/D unit. Synchronous operation releases the limitation on PWM modulation ratio and guar-antees that the electrical noises resulting from the switching operation of IGBTs do not interfere with the sampledcurrent. The simulation and experimental results verify the satisfactory performance of the proposed method.
PLMPC - supervisor predictive control; PLMPC - controle supervisorio preditivo
Energy Technology Data Exchange (ETDEWEB)
Ferreira, Amalia Burger Santa Brigida; Matuck, Fuad Jorge [White Martins S.A., Rio de Janeiro, RJ (Brazil)
2010-07-01
MPC is the latest and most sophisticated technology for controlling chemical plants with several interactive variables. Since 1984, over 2000 MPC systems have been installed worldwide, mostly at oil refineries and large petrochemical facilities. Praxair was the first company to apply MPC technology to the air separation industry. MPC technology is now Praxair's standard platform for supervisory control of cryogenic air separation plants. Most new Praxair plants are controlled by MPC systems. The Pipeline MPC (PLMPC) drives at least 2 plants, A and B, GO2 production towards optimum targets during the pipeline variations. The purpose of the PLMPC is to optimize gas oxygen (GO2) production according to demand, while ensuring a quickly pipeline response. It is implemented using AspenTech DMCPlus software, which is configured with a model file and a controller configuration file, that executes periodically. (author)
The Merlin Control Language for strategic optimization
Papageorgiou, D. G.; Demetropoulos, I. N.; Lagaris, I. E.
1998-04-01
MCL is the programming language of the MERLIN optimization environment. It can be used for the implementation of efficient optimization strategies, abolishing to a great extend the need for user intervention. The language is simple to learn and its structure is similar to Fortran. We report on successful applications where MCL played an instrumental role, as for example in molecular physics problems and in the training of neural networks.
Self-Tuning of Design Variables for Generalized Predictive Control
Lin, Chaung; Juang, Jer-Nan
2000-01-01
Three techniques are introduced to determine the order and control weighting for the design of a generalized predictive controller. These techniques are based on the application of fuzzy logic, genetic algorithms, and simulated annealing to conduct an optimal search on specific performance indexes or objective functions. Fuzzy logic is found to be feasible for real-time and on-line implementation due to its smooth and quick convergence. On the other hand, genetic algorithms and simulated annealing are applicable for initial estimation of the model order and control weighting, and final fine-tuning within a small region of the solution space, Several numerical simulations for a multiple-input and multiple-output system are given to illustrate the techniques developed in this paper.
Explicit Nonlinear Model Predictive Control Theory and Applications
Grancharova, Alexandra
2012-01-01
Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...
Ruiz-Cruz, Riemann; Sanchez, Edgar N; Ornelas-Tellez, Fernando; Loukianov, Alexander G; Harley, Ronald G
2013-12-01
In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms.
Robust output LQ optimal control via integral sliding modes
Fridman, Leonid; Bejarano, Francisco Javier
2014-01-01
Featuring original research from well-known experts in the field of sliding mode control, this monograph presents new design schemes for implementing LQ control solutions in situations where the output system is the only information provided about the state of the plant. This new design works under the restrictions of matched disturbances without losing its desirable features. On the cutting-edge of optimal control research, Robust Output LQ Optimal Control via Integral Sliding Modes is an excellent resource for both graduate students and professionals involved in linear systems, optimal control, observation of systems with unknown inputs, and automatization. In the theory of optimal control, the linear quadratic (LQ) optimal problem plays an important role due to its physical meaning, and its solution is easily given by an algebraic Riccati equation. This solution turns out to be restrictive, however, because of two assumptions: the system must be free from disturbances and the entire state vector must be kn...
An Efficient Constrained Model Predictive Control Algorithm Based on Approximate Computation
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The on-line computational burden related to model predictive control (MPC) of large-scale constrained systems hampers its real-time applications and limits it to slow dynamic process with moderate number of inputs. To avoid this, an efficient and fast algorithm based on aggregation optimization is proposed in this paper. It only optimizes the current control action at time instant k, while other future control sequences in the optimization horizon are approximated off-line by the linear feedback control sequence, so the on-line optimization can be converted into a low dimensional quadratic programming problem. Input constraints can be well handled in this scheme. The comparable performance is achieved with existing standard model predictive control algorithm. Simulation results well demonstrate its effectiveness.
Optimization and Convergence of Observation Channels in Stochastic Control
Yüksel, Serdar
2010-01-01
This paper studies the optimization of observation channels (stochastic kernels) in partially observed stochastic control problems. In particular, existence, continuity, and convexity properties are investigated. Continuity properties of the optimal cost in channels are explored under total variation, setwise convergence and weak convergence. Sufficient conditions for sequential compactness under total variation and setwise convergence are presented. It is shown that the optimization is concave in observation channels. This implies that the optimization problem is non-convex in quantization/coding policies for a class of networked control problems. Applications in optimal quantizer/coder design and robust control are presented, where new results on the existence of optimal quantizers are obtained. Furthermore, the paper explains why a class of decentralized control problems, under the non-classical information structure, is non-convex when {\\em signaling} is present. Finally, empirical con sistency of a class...
Optimal control of stochastic difference Volterra equations an introduction
Shaikhet, Leonid
2015-01-01
This book showcases a subclass of hereditary systems, that is, systems with behaviour depending not only on their current state but also on their past history; it is an introduction to the mathematical theory of optimal control for stochastic difference Volterra equations of neutral type. As such, it will be of much interest to researchers interested in modelling processes in physics, mechanics, automatic regulation, economics and finance, biology, sociology and medicine for all of which such equations are very popular tools. The text deals with problems of optimal control such as meeting given performance criteria, and stabilization, extending them to neutral stochastic difference Volterra equations. In particular, it contrasts the difference analogues of solutions to optimal control and optimal estimation problems for stochastic integral Volterra equations with optimal solutions for corresponding problems in stochastic difference Volterra equations. Optimal Control of Stochastic Difference Volterra Equation...
Optimal Control of Vehicular Formations with Nearest Neighbor Interactions
Lin, Fu; Jovanović, Mihailo R
2011-01-01
We consider the design of optimal localized feedback gains for one-dimensional formations in which vehicles only use information from their immediate neighbors. The control objective is to enhance coherence of the formation by making it behave like a rigid lattice. For the single-integrator model with symmetric gains, we establish convexity, implying that the globally optimal controller can be computed efficiently. We also identify a class of convex problems for double-integrators by restricting the controller to symmetric position and uniform diagonal velocity gains. To obtain the optimal non-symmetric gains for both the single- and the double-integrator models, we solve a parameterized family of optimal control problems ranging from an easily solvable problem to the problem of interest as the underlying parameter increases. When this parameter is kept small, we employ perturbation analysis to decouple the matrix equations that result from the optimality conditions, thereby rendering the unique optimal feedb...
Application of generalized predictive control in networked control system
Institute of Scientific and Technical Information of China (English)
YANG Can; ZHU Sha-nan; KONG Wan-zeng; LU Li-ming
2006-01-01
A new framework for networked control system based on Generalized Predictive Control (GPC) is proposed in this paper. Clock-driven sensors, event-driven controller, and clock-driven actuators are required in this framework. A queuing strategy is proposed to overcome the network induced delay. Without redesigning, the proposed framework enables the existing GPC controller to be used in a network environment. It also does not require clock synchronization and is only slightly affected by bad network condition such as package loss. Various experiments are designed over the real network to test the proposed approach,which verify that the proposed approach can stabilize the Networked Control System (NCS) and is robust.
Optimal Control for a Class of Chaotic Systems
Directory of Open Access Journals (Sweden)
Jianxiong Zhang
2012-01-01
Full Text Available This paper proposes the optimal control methods for a class of chaotic systems via state feedback. By converting the chaotic systems to the form of uncertain piecewise linear systems, we can obtain the optimal controller minimizing the upper bound on cost function by virtue of the robust optimal control method of piecewise linear systems, which is cast as an optimization problem under constraints of bilinear matrix inequalities (BMIs. In addition, the lower bound on cost function can be achieved by solving a semidefinite programming (SDP. Finally, numerical examples are given to illustrate the results.
PID control for chaotic synchronization using particle swarm optimization
Energy Technology Data Exchange (ETDEWEB)
Chang, W.-D. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)], E-mail: wdchang@mail.stu.edu.tw
2009-01-30
In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.
Nonconvex Model Predictive Control for Commercial Refrigeration
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F.S.; Jørgensen, John Bagterp
2013-01-01
is to minimize the total energy cost, using real-time electricity prices, while obeying temperature constraints on the zones. We propose a variation on model predictive control to achieve this goal. When the right variables are used, the dynamics of the system are linear, and the constraints are convex. The cost...... the iterations, which is more than fast enough to run in real-time. We demonstrate our method on a realistic model, with a full year simulation and 15 minute time periods, using historical electricity prices and weather data, as well as random variations in thermal load. These simulations show substantial cost...
CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Dr.A.TRIVEDI
2011-04-01
Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.
Stochastic Optimal Control for Series Hybrid Electric Vehicles
Energy Technology Data Exchange (ETDEWEB)
Malikopoulos, Andreas [ORNL
2013-01-01
Increasing demand for improving fuel economy and reducing emissions has stimulated significant research and investment in hybrid propulsion systems. In this paper, we address the problem of optimizing online the supervisory control in a series hybrid configuration by modeling its operation as a controlled Markov chain using the average cost criterion. We treat the stochastic optimal control problem as a dual constrained optimization problem. We show that the control policy that yields higher probability distribution to the states with low cost and lower probability distribution to the states with high cost is an optimal control policy, defined as an equilibrium control policy. We demonstrate the effectiveness of the efficiency of the proposed controller in a series hybrid configuration and compare it with a thermostat-type controller.
Optimal Vibration Control for Tracked Vehicle Suspension Systems
Directory of Open Access Journals (Sweden)
Yan-Jun Liang
2013-01-01
Full Text Available Technique of optimal vibration control with exponential decay rate and simulation for vehicle active suspension systems is developed. Mechanical model and dynamic system for a class of tracked vehicle suspension vibration control is established and the corresponding system of state space form is described. In order to prolong the working life of suspension system and improve ride comfort, based on the active suspension vibration control devices and using optimal control approach, an optimal vibration controller with exponential decay rate is designed. Numerical simulations are carried out, and the control effects of the ordinary optimal controller and the proposed controller are compared. Numerical simulation results illustrate the effectiveness of the proposed technique.
Fuzzy controller based on chaos optimal design and its application
Institute of Scientific and Technical Information of China (English)
邹恩; 李祥飞; 张泰山
2004-01-01
In order to overcome difficulty of tuning parameters of fuzzy controller, a chaos optimal design method based on annealing strategy is proposed. First, apply the chaotic variables to search for parameters of fuzzy controller, and transform the optimal variables into chaotic variables by carrier-wave method. Making use of the intrinsic stochastic property and ergodicity of chaos movement to escape from the local minimum and direct optimization searching within global range, an approximate global optimal solution is obtained. Then, the chaos local searching and optimization based on annealing strategy are cited, the parameters are optimized again within the limits of the approximate global optimal solution, the optimization is realized by means of combination of global and partial chaos searching, which can converge quickly to global optimal value. Finally, the third order system and discrete nonlinear system are simulated and compared with traditional method of fuzzy control. The results show that the new chaos optimal design method is superior to fuzzy control method, and that the control results are of high precision, with no overshoot and fast response.
Directory of Open Access Journals (Sweden)
J Lucas McKay
Full Text Available Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3 across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2× compared to individual muscle control. Our results are consistent with the idea that hierarchical, task
Robustified time-optimal control of uncertain structural dynamic systems
Liu, Qiang; Wie, Bong
1991-01-01
A new approach for computing open-loop time-optimal control inputs for uncertain linear dynamical systems is developed. In particular, the single-axis, rest-to-rest maneuvering problem of flexible spacecraft in the presence of uncertainty in model parameters is considered. Robustified time-optimal control inputs are obtained by solving a parameter optimization problem subject to robustness constraints. A simple dynamical system with a rigid-body mode and one flexible mode is used to illustrate the concept.
ON THE OPTIMAL CONTROL COMPUTATION OF LINEAR SYSTEMS
Directory of Open Access Journals (Sweden)
H. Tjahjana
2012-05-01
Full Text Available In this paper, we consider a numerical method for designing optimal controlon Linear Quadratic Regulator (LQR problem. In the optimal control design process through Pontryagin Maximum Principle (PMP, we obtain a system of diferential equations in state and costate variables. This system lacks of initial condition on the adjoint variables, and this situation creates classic dificulty for solving optimal control problems.This paper proposes a constructive method to approximate the initial condition of the adjoint system.
Optimal Control with Time Delays via the Penalty Method
Directory of Open Access Journals (Sweden)
Mohammed Benharrat
2014-01-01
Full Text Available We prove necessary optimality conditions of Euler-Lagrange type for a problem of the calculus of variations with time delays, where the delay in the unknown function is different from the delay in its derivative. Then, a more general optimal control problem with time delays is considered. Main result gives a convergence theorem, allowing us to obtain a solution to the delayed optimal control problem by considering a sequence of delayed problems of the calculus of variations.
Combined Active and Reactive Power Control of Wind Farms based on Model Predictive Control
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Wang, Jianhui;
2017-01-01
This paper proposes a combined wind farm controller based on Model Predictive Control (MPC). Compared with the conventional decoupled active and reactive power control, the proposed control scheme considers the significant impact of active power on voltage variations due to the low X=R ratio...... of wind farm collector systems. The voltage control is improved. Besides, by coordination of active and reactive power, the Var capacity is optimized to prevent potential failures due to Var shortage, especially when the wind farm operates close to its full load. An analytical method is used to calculate...... the sensitivity coefficients to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both normal and emergency conditions. A wind farm with 20 wind turbines was used to verify the proposed combined control scheme....
TayyebTaher, M.; Esmaeilzadeh, S. Majid
2017-07-01
This article presents an application of Model Predictive Controller (MPC) to the attitude control of a geostationary flexible satellite. SIMO model has been used for the geostationary satellite, using the Lagrange equations. Flexibility is also included in the modelling equations. The state space equations are expressed in order to simplify the controller. Naturally there is no specific tuning rule to find the best parameters of an MPC controller which fits the desired controller. Being an intelligence method for optimizing problem, Genetic Algorithm has been used for optimizing the performance of MPC controller by tuning the controller parameter due to minimum rise time, settling time, overshoot of the target point of the flexible structure and its mode shape amplitudes to make large attitude maneuvers possible. The model included geosynchronous orbit environment and geostationary satellite parameters. The simulation results of the flexible satellite with attitude maneuver shows the efficiency of proposed optimization method in comparison with LQR optimal controller.
Hocker, David Lance
The control of quantum systems occurs across a broad range of length and energy scales in modern science, and efforts have demonstrated that locating suitable controls to perform a range of objectives has been widely successful. The justification for this success arises from a favorable topology of a quantum control landscape, defined as a mapping of the controls to a cost function measuring the success of the operation. This is summarized in the landscape principle that no suboptimal extrema exist on the landscape for well-suited control problems, explaining a trend of successful optimizations in both theory and experiment. This dissertation explores what additional lessons may be gleaned from the quantum control landscape through numerical and theoretical studies. The first topic examines the experimentally relevant problem of assessing and reducing disturbances due to noise. The local curvature of the landscape is found to play an important role on noise effects in the control of targeted quantum unitary operations, and provides a conceptual framework for assessing robustness to noise. Software for assessing noise effects in quantum computing architectures was also developed and applied to survey the performance of current quantum control techniques for quantum computing. A lack of competition between robustness and perfect unitary control operation was discovered to fundamentally limit noise effects, and highlights a renewed focus upon system engineering for reducing noise. This convergent behavior generally arises for any secondary objective in the situation of high primary objective fidelity. The other dissertation topic examines the utility of quantum control for a class of nonlinear Hamiltonians not previously considered under the landscape principle. Nonlinear Schrodinger equations are commonly used to model the dynamics of Bose-Einstein condensates (BECs), one of the largest known quantum objects. Optimizations of BEC dynamics were performed in which the
Pseudospectral Optimal Control: Hidden Properties and Flight Results
2011-11-30
on solving optimal control problems , we focus on developing PS methods over arbitrary grids for Problem B. Such research can provides a unified...more efficient algorithms for solving optimal control problems , for example, multiscale PS methods for dynamical systems with different timescales
Educational Tool for Optimal Controller Tuning Using Evolutionary Strategies
Carmona Morales, D.; Jimenez-Hornero, J. E.; Vazquez, F.; Morilla, F.
2012-01-01
In this paper, an optimal tuning tool is presented for control structures based on multivariable proportional-integral-derivative (PID) control, using genetic algorithms as an alternative to traditional optimization algorithms. From an educational point of view, this tool provides students with the necessary means to consolidate their knowledge on…
On-line optimal control of greenhouse crop cultivation.
Straten, van G.
1996-01-01
Thus far, optimal control has primarily been investigated for seasonal crop growth optimization. On-line aspects have received much less attention. The decomposition between long term strategies and on-line control, however, is not trivial. Appreciable losses occur when set-points generated by seaso
On-line optimal control of greenhouse crop cultivation.
Straten, van G.
1996-01-01
Thus far, optimal control has primarily been investigated for seasonal crop growth optimization. On-line aspects have received much less attention. The decomposition between long term strategies and on-line control, however, is not trivial. Appreciable losses occur when set-points generated by seaso
Stochastic optimal control of partially observable nonlinear quasi-integrable Hamiltonian systems
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The stochastic optimal control of partially observable nonlinear quasi-integrable Hamiltonian systems is investigated. First, the stochastic optimal control problem of a partially observable nonlinear quasi-integrable Hamiltonian system is converted into that of a completely observable linear system based on a theorem due to Charalambous and Elliot. Then, the converted stochastic optimal control problem is solved by applying the stochastic averaging method and the stochastic dynamical programming principle. The response of the controlled quasi Hamiltonian system is predicted by solving the averaged Fokker-Planck-Kolmogorov equation and the Riccati equation for the estimated error of system states. As an example to illustrate the procedure and effectiveness of the proposed method, the stochastic optimal control problem of a partially observable two-degree-of-freedom quasi-integrable Hamiltonian system is worked out in detail.
A SIMPLIFIED PREDICTIVE CONTROL FOR A SHELL AND TUBE HEAT EXCHANGER
Directory of Open Access Journals (Sweden)
S.RAJASEKARAN,
2010-12-01
Full Text Available In this paper a simplified predictive control design is applied for the controlling a temperature of a fluid stream using the shell and tube heat exchanger. The predictive control design based on Dynamic Matrix Control (DMC involves the complicated inversion computation for higher dimensional matrix. Using DMC for controlling a temperature of the shell and tube heat exchanger, there is still a need for optimization of conversation of energy. The simplified predictive control is based on DMC, which reduces the computational complexity by exploring its internal mechanism. Finally the simplified Predictive Control is applied to shell and tube heat exchanger and the results of this control algorithm compared with the conventional PID controller and DMC based PID Controllers.
Exploiting Higher-order Derivatives in Computational Optimal Control
Ross, I. Michael; Rea, Jeremy; Fahroo, Fariba
2002-01-01
Proceedings of the 10th Mediterranean Conference on Control and Automation -- MED 2002 , Lisbon, Portugal, July7 9-12, 2002 To facilitate generation of real-time solutions to nonlinear optimal control problems, we present a new way of approximating higher-order derivatives that arise in control systems. A Legendre pseudospectral method is presented to efficiently and accurately discretize optimal control problems governed by higher-order dynamical constraints. For mechanical systems, a re...
Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa
2013-04-09
Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.
Kolossváry, István
2012-01-01
We propose a new way of looking at global optimization of off-lattice protein models. We present a dual optimization concept of predicting optimal sequences as well as optimal folds. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We emphasize that while the protein model we used is minimalist, the methodology is applicable to detailed protein models, and sequence optimization may yield novel folds and aid de novo protein design.